Ha NQi-2009

Ngimh: Cong Ngh~ Thong Tin

KHOA LU~ TOT NGID:P D~ HQC H: cHiNHQUY

pIlAT BIEN MAT NGUm TRONG ANB. .VAUNGDUNG.

LeM~nh TuAn

----03{>'&>----

D~ HQC CONG NGII

D~ HQC ouoc GIA IIA NQI

HA NOI - 2009

Ngimh: Cong ngh~ thong tin

Can bC)hmrng dn: TS. Nguyin Hili Chiiu

KHoA LU~ TOTNGHIP D~ HQC H cHiNH QUY

pIlAT BIEN MAT NGU01 TRONG ANB VAUNGDUNG.

J)~ HQC ouoc GIAHA xorTRUONG J)~ HQC CONGNGH

iii

Ha n{ji, ngay 24 thdng 5 niim 2009Sinh vien

isMlJnh Tuan

Sau mot thai gian dai h9C tg,p va nghien cuu, cu6i cung em ciing do' hoan thanh.khoa ludn t6t nghiep nay, ddy 10.dip t6t nhat di em co thi gUi loi cam an din moi nguoi.

Em xin gui loi cam an sdu siic din Tiin sf Nguyfn Hai Chdu, da tdn hinh huangdan, dinh huang cho em trong su6t thai gian thuc hien di tai. Thay da cho em nhiing loikhuyen quy bau giup em hoan thanh t6t khoa ludn.

Em xin cam an khoa Cong Ngh Thong Tin truong Dai H9C Cong Ngh - Dai H9CQu6c Gia Ha Noi, cam an cac thay co trong khoa da tdn tinh giang ddy, truyen dat choem nhiing kdn thuc quy bau trong nhiing niim h9C vita qua, giup cho em co mdt nin tangkiin thtec vieng chiic di thuc hien khoa ludn ciing nhu nghien cuu h9C tdp sau nay.

Toi xin cam an tat ca ban be, anh, chi, nhiing nguai da giup dil, khich l ciing nhuphe binh, gop y, giup toi hoan thann khoa ludn mdt cacli t6t nhat.

Cu6i cung, con cam an b6, me, nhiing nguoi luon luon quan tdm, cham soc cho conca vi vdt chat Ian tinh than, luon tao diiu kien t6t nhat cho con co thi chuyen tdm h9Ctdp, nghien cuu. Gia dinh luon 10.nguon d{jng vien, 10.ch6 dua tinh than vieng chiic chocon.

LOi cam on

iv

Ngay nay cac h~ thong nhan dang m~t nguoi dang diroc irng dung trong nhiSu linhVl,IC, d~c biet la an ninh va bao m~t. Nhan dang m~t nguoi nrc la dua ra nhirng thong tinvS d6i tuong diroc dua van illm(}t birc anh, hay ill camera quan sat. Biroc d~u tien cuamot h~ thong nhan dang m~t ngiroi la phat hien khuon mat, nrc xac dinh vi tri khuon m~ttren birc anh, sau day tach khuon m~t ra khoi anh d~ tien hanh nhan dang.

Trong kh6a luan nay em xin neu mot trong nhfmg phirong phap d~ phat hien khuonm~t trong anh, sau day rna rong ra voi phat hien m~t nguoi trong video va webcam.

Sau khi dff xem xet cac huang tiep c~n khac nhau, em chon phirong phap tiep c~nhoc may b~ng Adaboost va mo hinh Cascade of classifiers. Diem manh cua Adaboost lat6c d(}phat hien khuon m~t kha nhanh. Ket qua tlnr nghiem cho thay chuang trinh dat d(}chinh xac kha cao voi cac anh mat nguoi diroc chup true dien bang may ki thuat s6.

TOM TAT

v1. T6ng quan v~ Adaboost. 231.1.Ti~p c~ Boosting 231.2. Adaboost 241.3. Cac d~c tnmg Haar-Like 281.4. Cascade of Classifiers 31

1. Gioi thieu v~ bai toan xac dinh m~t ngiroi trong anh 32. Dinh nghia bai toan xac dinh m~t ngiroi 33. Nhfrng kh6 khan va thach tlnrc d6i voi bai toan xac dinh m~t nguoi 34. Cac img dung cua xac dinh m~t nguoi 44.1. Xac minh tQipham 44.2. Camera chong trom 44.3. Bao m~t. 44.4. Liru trir khu6n m~t 44.5. Cac img dung khac 5

5. Xac dinh pham vi d~tai 5A " " ",Chuang 2 C C CONG TRINH NGHIEN ctru 7

1. Cac phuong phap chinh d~xac dinh m~t ngiroi 71.1. Huang tiep c~n dua tren tri thirc 81.2. Huang ti~p can dua tren d~c tnrng kh6ng thay d6i 101.3. Huang ti~p can dira tren so sanh khop m:u 131.4. Huang ti~p c~ dua tren dien mao 16

O '.' ..tChuang 3 C SO LY THUYET 23

M1,JCLl)C v

DANH SACH THUAT NGU vii

DANH M1,JCHi:NH vB viii

DANH SACH TUVrET TAT ix

MODAu 1

Chuang 1 TONG QUAN vB cAc PHUONG PHAP xAc DJNH MAT NGVOI 3

TOMTAT iv

MUCLUC

vi

1.5. Cascade of boosting classifiers 33Chuang 4 xAY DVNG UNG Dl)NG 34

1. SO'hroc vS OpenCV 342. Tong quan vS mot h~ thong phat hien m~t nguoi trong anh 353. Phan tich - thiet kSM thong phat hien m~t nguoi 353.1.Phan tich 353.2. ThiSt kS h~ thong 363.3. ThiSt kS co sa dfr li~u 383.4. ThiSt kS giao dien 39

KET LuANvA HUONG PIiAT TRIEN .41

vii

Thu~t ngfr Ghli thichClassifier M9t b9 phan loai duoc xay dung theo m9t thuat toan hoc may

(b9 phan loai) nao day, dung dS phan loai cac d6i tuong (khuon m~t haykhong phai khuon mat)

False alarm La ti l~ nhan dang sai cua cac b9 phan loaiFeature Cac thong tin giup nh~n biet doi nrong.

(d~c trung)Haar-like feature Cac d~c trung cua doi nrong trong anh. OlC d~c trung nay

tlnrong duoc dinh nghia bang cac tinh toan voi t6ng diSm anhcua mot vung nao do tren birc anh.

Hit rate La ti l~ nhan dang dung cua b9 pharr loaiMax false alarm Dat diroc ti l~ sai nay thi b9 pharr loai diroc xay dung thanh

cong, ti l~ sai nay la m9t gia tri be, co thS chap nhan diroctrong bai toan phan loai.

Strong classifier B9 phan loai duoc xay dung ill nhieu b9 phan loai yeu, co d9(b9 phan loai manh) chinh xac cao.

Threshold Ngufrng la gia tri ranh gioi gifra cac lap, gia tri cua ngirong(ngu6ng) co thS di~u chinh diroc thuong duoc chon ill thirc nghiem.

(Nguoi ta thir b9 nhan dang voi cac gia tri ngu6ng khac nhaudS chon ra ngu6ng cho ti l~ nhan dang dung t6t nhat)

Weak classifier B9 pharr loai don gian co d9 chinh xac khoang 50%.(b9 pharr loai ySu)

DANHSAcHTHUATNGU

viii

Hinh 20: Giao dien cua chuong trinh .40

Hinh 14: Cascade of boosting classifiers 33

Hinh 15: C~u true co ban cua OpenCV 34

Hinh 16: T6ng quan v h~ thong phat hien m~t nguoi trong anh 35

Hinh 17: BiSu d6 ngit canh cua h~ thong 36

Hinh 18: Bieu d6 phan ra chirc nang 38

Hinh 19: SO'd6 thuc thS quan M (ERM ) 39

Hinh 13: cascade of classifiers 32

Hinh 10: Cach tinh Integral Image cua anh 30

Hinh 11:Vi du each tinh nhanh t6ng cac di~m anh cua vung D tren anh 30

Hinh 12: Vi du each tinh nhanh t6ng diSm anh cua vung D tren anh voi cac d~c trungxoay 45 30

Hinh 9: Cac d~c trung rna rong cua cac d~c trung Haar-like co sa 29

Hinh 7: Boosting 24

Hinh 8 : 4 d~t trung Haar-like co ban 28

Hinh 1: M9t phuong phap xac dinh khuon m~t di~n thea huong ti~p c~ top-down 8

Hinh 2: Phuong phap chi~u 10

Hinh 3: M9t mau khuon m~t 15

Hinh 4: cac vector quan sat dS hu~n luyen cho HMM 20

Hinh 5: Cac trang thai iin 21

Hinh 6: Xac dinh khuon m~t bang HMM 21

DANH MUC HiNH vi:

ix

STT Ki hi~u Til' ti~ng anh Y nghia1 CSDL Database Co sa dfr lieu2 HMM Hidden Markov Model M6 hinh Markov an3 ML Maximum-Likelihood Phuong tlnrc cue dai kha nang4 PCA Principal Component Analysis Phan tich thanh phan chinh5 PDF Probility Density Ham m~t d9 xac xuat6 RSAT Rotated Summed Area Table Mang hai chieu dung de tinh

nhanh cac d~c trung xoay 45

7 SAT Summed Area Table Mang hai chieu dung de tinhnhanh cac d~c trung haar-like coban

DANH SACH TU VIET TAT

1Cong nghe thong tin dang duoc irng dung trong moi Iinh V\ICcua cuoc song, VoimQt h~ thong may tinh, chung ta co th~ lam duoc r~t nhieu viec, ti~t kien thai gian vacong sire. Di~n hinh nlnr cong viec nhan dang m~t nguoi. Ngay xua, mu6n tim ki~m motke tinh nghi trong sieu thi hay san bay, cac nhan vien an ninh phai tim kiem tren nrng manhinh camera thea doi. Ngay nay, cong viec d~y da diroc lam nr dQng nho cac M thongnhan dang m~t nguoi. Phat hien m~t nguoi trong anh la mQt phAn quan trong cua M thongnhan dang m~t nguoi do, giai quyet t6t viec phat hien m~t nguoi se giup ti~t kiem thaigian va nang cao dQchinh xac cua viec nhan dang khuon m~t.

Phat hien m~t nguoi cling la mQt bai toan nhan dang dan gian, h~ thong chi dn phanloai d6i nrong dua vao co phai m~t nguoi hay khong phai m~t nguoi. d rmrc dQcao han,sau khi da phat hien duoc khuon mat, cac khuon m~t do se duoc so sanh voi cac khuonm~t co trong dfr lieu d~ nhan dang xem khuon m~t d~y la cua ai (thuong ap dung trongnhan dang khuon mat cua nguoi n6i tieng hoac cua tQipham dang bi truy na).

Bai toan phat hien m~t ngiroi duoc bilt dAu nghien CUuill nhfrng nam 1990s, va daco r~t nhieu cong trinh nghien ciru v~ phat hien khuon m~t trong anh, tuy nhien eho d~nnay, cac nha khoa hoc vin khong ngirng tim cac huang ti~p e~n moi, cac thuat toan moinham nang eao hieu su~t cua viec phat hien khuon m~t cling nlnr viec nhan dang m~tnguoi.

V6i muc tieu chinh la tim hi~u giai thuat adaboost, cac d~e tnrng haar-like, mo hlnhCascade of Classifiers, d6ng thai ap dung vao bai toan phat hien m~t nguoi trong anh,khoa luau duoc trinh bAytrong b6n chuang voi b6 C\1Cnhu sau:

ChU'ong 1:TAng quan v~ cac phU'ong phap xac djnh m~t ngU'Oi : Gi6i thi~u t6ngquan v~ bai toan xae dinh m~t nguai trong anh, cae Ung d\illg va nhUng kho khan eua baitoan, d6ng thai xac dinh ph~m vi eua d~ tai.

ChU'ong 2: Cac cong trinh nghien cuu : Neu chi ti~t bai toan phat hi~n m~t nguai,cac huang ti~p e~n giai quy~t bai toan, cac nghien eUu va thanh qua d~t duqe eua cac nhanghien cUu trong bai toan xac dinh m~t nguai.

MODAU

2ChU'O'Dg3: CooslY Iy thuyet : Di sau vao lnrong tiSp c~n dira thea thuat toan hocmay adaboost. Gioi thieu vB cac d~c trung haar-like cua khuon m~t, each tinh cac d~ctrung haar-like. TiSp thea la gioi thieu vB mo hinh cascade of classifiers va each ap dungvao bai toan phat hien m~t nguoi trong anh.

ChU'O'Dg4: Xay dung ung dung : Xay dung mot chuong trinh demo vB phat hienm~t nguoi trong anh. Neu len cac phan tich - thiSt kS vB chuong trinh.

Cu6i cling la k~t Iu~n va hueng phat tri~n: Tom tit nhfrng kSt qua dat duoc,nhfmg han chS va neu len cac hirong phat trien trong nrong lai.

3Xac dinh khuon m~t nguoi Ii mQt leY thuat may tinh d~ xac dinh cac vi tri vi kichthuoc cua cac khuon mat nguoi trong cac anh b~t ki. Ky thuat nay nhan biSt cac d~c trungcua khuon m~t vi be qua nhirng thir khac nhu: toa nha, cay c6i, C(J th~ ...

3. Nhfrng kho khan vil thach tlnrc dAiVOlbal toan xac djnh m~t nguot,

Vi~c xac dinh khuon m~t nguoi co nhfrng kho khan nh~t dinh nhu:

Huang cua khuon m~t d6i voi may anh, nhu: nhin thang, nhin nghieng hay nhinill tren xuong. Cung trong mot anh co th~ co nhieu khuon m~t a nhfrng tu thSkhac nhau.

Su co m~t cua cac chi tiSt khong phai Ii d~c trung rieng cua khuon m~t nguai,nhu: rau quai non, m~t kinh, ....

Cac net m~t khac nhau tren khuon m~t, nhu: vui, bu5n, ng~c nhien, ....

M~t nguai bi che khu~t boo cac d6i tuQ'llgkhac co trong anh.

2. Djnh nghia bili toan xac djnh m~t ngufri,

Trong nhieu nam qua, co d.t nhieu cong trinh nghien ciru v~ bai toan nhan dang m~tnguoi. Cac nghien ciru di ill bai toan dan gian, ill viec nhan dang mot m~t nguoi tronganh den trang cho dSn rna rong cho anh mAuva co nhieu m~t nguoi trong anh. DSn naycac bai toan xac dinh m~t nguoi dff rna rong voi nhi~u rni~n nghien ciru nhu nhan dangkhuon m~t, dinh vi khuon m~t, theo doi m~t ngiroi hay nhan dang cam xuc m~t nguoi ...

Phat hien m~t nguoi trong anh la phAn dAu tien cua mQt h~ thong nhan dang m~tnguoi. Cac h~ thong nhan dang khuon m~t duoc b~t dAu xay dung ill nhfrng nam 1970,tuy nhien do con han chS v~ cac lu~t xac dinh m~t ngiroi nen chi dtroc ap dung trong mQts6 img dung nhu nhan dang the can cuoc. No chi duoc phat tri~n manh me illnhfrng nam1990 khi co nhirng tiSn bQ trong cong nghe video vi ngay nay thi cac irng dung cua xacdinh m~t nguoi dfftro nen ph6 bien trong cuoc song,

1. GiOlthi~u v~bal toan xac djnh m~t ngrrOitrong anh,

Chuong 1TONG QUAN vi: cAc PHUONG pHAp

xAc DINH MAT NGUm. .

44.3. Bao m~t.

Cac irng dung vBbao m~t r~t da dang, mot trong s6 do la cong nghe nhan dang m~tnguoi cua laptop, cong nghe nay cho phep chu nhan cua may tinh chi cAnng6i truce mayla co thS dang nhap duoc. )S su dung cong nghe nay, ngiroi dung phai su dung m9twebcam dS chup anh khuon m~t cua minh va cho may "hoc" thuoc cac d~c diSm cuakhuon m~t giup cho qua trinh dang nh~p sau nay.

4.4. LU'Utrfr khuon m~t

Xac dinh m~t ngiroi co thS diroc irng dung trong cac tram rut tiBn tv dong (ATM) dS1uu trfr khuon m~t cua nguoi rut tiBn.Hien nay co nhfrng nguoi bi nguoi khac l~y trom theATM va rna PIN, va bi rut tiBn trom, hoac co nhfmg chu tai khoan di rut tiBn nhung laibao voi ngan hang la bi m~t the va bi rut tin tr9m. N~u luu trfr duQ'c khuon m~t cllanguai rut tiBn, ngan hang co thS d6i chirng va xu 1:9'd~ dang han.

4.1.Xac minh tQi pham.

Dua vao anh cua m9t ngiroi, nhan dang xem nguoi d~y co phai la t9i pham haykhong bing each so sach voi cac anh t9i pham dang diroc 1uu trfr. Hoac co thS su dungcamera dS phat hien t9i pham trong dam dong. lrng dung nay giup co quan an ninh quan1:9'con ngiroi t6t han.

4.2. Camera chang trQm.

Cac M thong camera se xac dinh dau la con nguoi va theo d5i xem con ngtroi do colam gi pham phap khong, vi du nhu l~y trom d6, xam nhap b~t hQ'Pphap vao m9t khu V\fCnao do.

Xac dinh m~t nguoi thuong la m9t phAn cua mot M thong (facial recognitionsystem). No thirong duoc dung trong giam sat video, giao ti~p ngiroi may va quan 1:9'cosa dfr lieu anh ... Cac irng co ban cua xac dinh m~t nguoi co thS kS d~n la:

4. Cac .rng dung cua xac dinh m~t nguot,

Su biSu cam cua khuon m~t : su biSu cam co thS lam thay d6i dang kS cac d~ctnrng va thong s6 cua khuon m~t, vi du nhu khuon m~t cua cung m9t nguoi ser~t khac khi nguoi d~y cuoi, nrc gi~n hay SQ'hai ...

5Trong dS tai nay, toi t~p trung vao viec xac dinh khuon m~t trong anh, video hoacwebcam. TiI d~y hru khuon m~t tim duoc vao CSDL dS phuc vu cho cac muc dich khac (chang han nhu nhan dang m~t nguoi hoac ghep khuon m~t vao birc anh khac ... ). Do cacdiSu kien kho khan da neu a tren ( muc 3 chuang 1) toi xin dua ra nhirng gia dinh va ranhbU9Csau dS giam d9 plnrc tap cua bai toan:

Cac khuon m~t duoc chup thang hoac goc nghieng khong dang kS (be han 10)

Phong nSn cua anh khong qua plnrc tap

Anh duQ'cch\lp trong diSu ki~n anh sang binh thuang.

5. Xac djnh pham vi d~tai

4.5. Cac tmg dung khac

DiSu khien vao ra: van phong, cong ty, tru sa, may tinh, Palm, .... K~t hQ'Pthemvan tay va mong milt. Cho phep nhan vien duoc ra vao noi can thi~t.

An ninh san bay, xu~t nhap canh (hien nay co quan xu~t nhap canh My da apdung). DUng camera quan sat dS xac thuc ngiroi nhap canh va kiSm tra xem nguoid~y co phai 1at9i pham hay phan illkhung b6 khong,

Tim ki~m va t6 chirc dfr lieu lien quan d~n con ngiroi thong qua khuon m~t ngiroitren nhieu h~ co sa dfr lieu hru trfr th~t 100, nhu internet, cac hang truyen hinh, ....Vi du: tim cac doan video co t6ng thong Bush phat bieu, tim cac phim co di~n vienTang Thanh Ha dong, tim cac tran da bong co Cong Vinh da, ...

KiSm tra trang thai nguoi lai xe co ngu g~t, m~t ~p trung hay khong, va h6 trothong bao khi can thiet.

Tuong 1ai se phat trien cac loai the thong minh co tich hQ'P sn d~c trung cuangiroi dung tren do, khi b~t cir ngiroi dung khac dung dS truy c~p hay xU 1ytai cach~ thong se duoc yeu cfiu kiSm tra cac d~c trung khuon m~t so voi the dS bi~t nayco phai 1achu the hay khong.

Hang may chup hinh Canon da img dung bai toan xac dinh khuon m~t ngiroi vaomay chup hinh th~ h~ moi dS cho k~t qua hinh anh dep han, nh~t la khuon m~tngiroi.

6 D6i voi video hoac webcarn, do co thS tach thanh cac xu ly tren anh nen voinhfrng video plnrc tap hay webcarn qua kern, chuong trinh se kh6ng thuc hiendiroc t6t nhfrt co thS.

7DVa vao tinh ch~t cua cac phirong phap xac dinh m~t nguoi tren anh, cac phirongphap nay duoc chia thanh b6n loai chinh, wong irng voi b6n huang tiSp c~ khac nhau.Ngoai ra ciing c6 r~t nhieu nghien ciru rna phuong phap xac dinh m~t nguoi khong chidua vao mot huang rna c6 lien quan dSn nhiSu huang.

Huang tiSp c~n dira tren tri thirc: Dua vao cac thuat toan, rna h6a cac d~c trung vaquan h~ giira cac d~c trung cua khuon m~t thanh cac lu~t. Day la huang tiSp c~nthee ki8u top-down.

Huang tiSp c~n dira tren d~c trung khong thay d6i: Xay dung cac thu~t toan d8 timcac d~c trung cua khuon m~t rna cac d~c trung nay khong thay d6i khi nr thSkhuon m~t hay vi tri d~t camera thay d6i. Huang tiSp c~n nay la huang tiSp c~nthee ki8u bottom-up.

Huang tiSp can dua tren so sanh khop miu: DUng cac miu chuan cua khuon m~t(cac miu nay diroc dinh nghia bang tay truce hoac diroc tham s6 h6a bang mQham s6) d8 mo ta cac khuon m~t hay cac d~c trung cua khuon m~t. Miu nay duocsft dung d8 phat hien khuon m~t b~ng each quet n6 qua anh va tinh toan gia triwong d6ng cho m6i vi tri. Vi~c xu~t hien mQt khuon m~t tai mot vi tri nao dotrong anh phu thuoc vao gia tri wong d6ng cua di8m d6 so voi miu chuan

Huang tiSp c~n dua tren dien mao: Trai ngiroc voi huang tiSp c~n dua tren khuonmiu, cac mo hinh (hay cac miu) se duoc hoc illmot t~p anh huan luyen rna th8hien tinh ch~t tieu bieu cua su xu~t hien cua m~t nguoi trong anh. Sau do h~th6ng (mo hinh) se xac dinh m~t ngiroi. Phuong phap nay con diroc biSt dSn voiten goi tiSp c~n thee cac phuong phap hoc may.

1. Cac phurrng phap ehinh d~ xac djnh m~t nguot,

ChU'ong2cAc CONG TRiNH NGHIEN crru

8Hinh 1: MQtphuong phap xac dinh khuon m~t diSn theo lnrong ti~p can top-down.

Tren hinh 1, Cac luat duoc xay dung dua vao tri tlnrc cua nguoi nghien cUu vS cacd~c trung cua khuon m~t (vi d\! nhu cuang dQphan phoi va sv khac nhau) cua cac vlingtren khuon m~t

1.1.2. Cac nghien ciru

Co nhiSu each dS xay dung chuong trinh xac dinh m~t ngiroi theo lnrong ti~p candua tren tri thirc. Thong thirong chuong trinh se duoc xay dung theo lnrong dua vao mQtphuong phap nao dAy chon ra cac irng vien trong birc anh tnroc, sau do se ap dung caclu~t d@xac dinh irng vien nao Ia khuon m~t, irng vien nao khong phai la khuon m~t. Quatrinh nay co th@diroc ap dung nhiSu IAndS giam sai sot.

1.1.1. Tu nrong

Trong lnrong ti~p can nay, cac phirong phap xac dinh m~t nguoi duoc xay dung diravao cac lu~t, cac lu~t nay phu thuoc vao tri thirc cua cac tac gia nghien ciru vS bai toanxac dinh khuon m~t. D~ dang xay dung cac lu~t co ban dS mo ta cac d~c trung cua khuonm~t va cac quan h~ nrong img. Vi du, mQt khuon m~t thirong co hai mlit d6i xirng quatrue thing dimg giira khuon m~t va co mQtmfii mot mieng. Cac quan h~ d~c trung co thSla quan h~ vS vi tri va khoang each nrong d6i. Kho khan cua hirong ti~p can nay do la lamth~ nao dS chuyen cac tri tlnrc cua con nguoi vS khuon m~t sang cac luat cho may tinhmQt each hieu qua. N~u cac lu~t nay qua chi ti~t, chat cM thi se co th xac dinh thieu cackhuon m~t co trong anh, nhung n~u cac luat qua tbng quat thi se d~n d~n xac dinh sai khuV1JC khong phai la khuon m~t thanh khuon m~t. Ngoai ra cling kho d rna rong pham vicua bai toan dS xac dinh cac khuon m~t co nhiSu nr th~ khac nhau.

1.1. Huong ti~p c~n dl}'atren tri tlnrc

9Dua tren bi~u db hinh chieu ngang, khi xet bi~u db bi~n thien ella HI hai ong timduoc hai cue ti~u dia plnrong tuong irng voi hai dinh d~u ben trai va ben phai ella khuonm~t. Con theo hinh chieu doc, xet bi~n thien va tim eve ti~u dia phuong ella VI cling ehota cac vi tri cua mieng, dinh mill va hai mit. Cac d~c trung nay dll d~ xac dinh khuon m~t.Hinh 2.a eho mQt vi du vS each xae dinh nhu tren. Caeh xae dinh nay e6 ty 1~xae dinhchinh xac 1a 86.5% cho truemg hQ'Pchi c6 mQt khuon m~t tr\]'e di~n va hinh nSn khongphue t~p. N~u hinh nSn phue t~p nhu hinh 2.b thi r~t kh6 tim. Con n~u anh e6 nhiSukhuon m~t (hinh 2.e) thi se khong xac dinh duQ'c.

Yang va Huang da su dung huang tiep e~n dua tren tri tlnrc d~ xac dinh khuon m~t[7]. H~ thong ella hai tac gia bao gbm ba rmrc 1u~t.D~u tien cac ong su dung mQt khungcira s6 d~ quet tren birc anh va thong qua mQt s6 t~p 1u~td~ xac dinh cac irng vien co th~la m~t ngiroi, vi du nhu tim phan trung tam ella khuon m~t (phan t6i hon trong hinh 1).Ti~p theo, dung mot t~p luat d~ mo ta t6ng quat hinh dang khuon mat, 1Qecac img vien 0rmrc mot thanh mot ~p cac irng vien moi co xac xuat la khuon m~t eao hon, Cu6i cung,cac ong lai dung mQt t~p 1u~tkhac d~ xem xet 0 mire chi ti~t cac d~e trung khuon m~t ( coth~ la d~e trung vS m~t, miii, mieng ... ), illd6 1Qera cac irng vien ehinh xac nh~t. C6 th~n6i each lam ella hai ong la mQt each lam min d~n d~ dat diroc k~t qua t61nh~t, m~e du ty1~ehinh xac chua eao, nhung day la tiSn dS eho nhiSu nghien ciru sau nay.

C6 r~t nhiSu cong trinh nghien ciru su dung phuong phap ehi~u d~ xac dinh khuonm~t. Kotropou1os va Pitas dua ra mot phirong phap g~n tuong nr voi Yang va Huang. D~utien, cac vung ella khuon m~t diroc dinh vi boi phirong phap chieu (da diroc Kanade sudung thanh cong) d~ xac dinh bien ella khuon m~t. Voi I(x,y) la gia tri xam ella mQt di~mtrong anh co kieh thiroc m x n 0 tai vi tri (x,y), cac ham d~ chieu anh theo plnrong ngangva thing dirng duoc dinh nghia nhu sau:

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Leung dff dua ra m9t mo hinh xac xu~t de xac dinh khuon m~t trong anh co hinh n~nplnrc tap dira tren viec tim kiem cac d~c trung khong thay d6i cua khuon m~t trong anh,sau do dung d6 thi ng[u nhien de xac dinh khuon m~t. Tir nrong cua phirong phap nay dola xem bai toan xac dinh khuon m~t nhir la bai toan tim kiem v&im\lC tieu Ia tim thu tvcac d~c trung khong thay d6i cua khuon m~t. DUng nam d~c trung (hai m~t, hai 16mill,phk n6i gifra mili va mi~ng) dB mo ta m9t khuon m~t. :E>6ngthai tinh quan h~ khoangcach cho t~t ca cac c~p d~c trung (nhu m~t trai, m~t phai), sau d~y dUng phan b6 Gauss dB

Hinh 2: Phuong phap chieu(a) AM chi co m9t khuon m~t va hinh n~n don gian;(b) AM chi co m9t khuon m~t va hinh n~n phirc tap;

(c) AM co nhieu khuon m~t1.2.Hurrng ti~p e~ndl}'atren dije trung khong tbay dAi.

Ti~p can dua tren cac d~c trung khong thay d6i la ti~p c~n theo kiBu bottom up, Duatren tlnrc t~, chung ta c6 gang tim ra nhirng d~c diBm khong thay d6i khi cua khuon m~tkhi a cac nr th~ khac nhau va cac di~u kien moi tnrong nhu anh sang khac nhau. :E>ffco r~tnhieu nghien ciru v~ lnrong tiep c~ nay, cac d~c diem khong thay d6i diroc tim th~y nhulong may, m~t, mfii, mieng hay cac duong vi~n tren toe '" Tren co sa cac d~c trung nay,cac nha nghien ciru xay dung len m9t mo hinh thong ke mo ta quan h~ cua cac d~c trungva tU do xac dinh su xu~t hien cua khuon m~t trong birc anh. Kho khan cua huong ti~pc~n nay do la phai mo ta cac d~c trung khong thay d6i va quan h~ giira chung phil hQ'Pvoidi~u kien anh sang, d9 nhiSu hay bi che khu~t.

1.2.1. Cac dije trung ella khuon mijt

Co r~t nhieu nghien ciru v~ cac d~c trung cua khuon m~t. Co thB dua vao cac d~ctrung nhu hai m~t, hai 16mfii, mieng, phAn n6i gifra mfii va mieng ... hoac dua vao duongvi~n cua khuon mat.

(elIb)(a)

,J" ,.....~ I",''U.'W

. t ..

- a--,. ,,;ott. - \ . it, II.,t ta" ,

( '\.)1,-..jl' '.

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mo hinh hoa. MQt m~u khuon mat duoc dua ra thong qua trung binh nrong irng cho mott~p da hirong, da ty l~ cua bQ loc dao ham Gauss. Til mQt anh, cac d~c trung cua img vienduoc xac dinh bang each so khop rung diem anh khi loc nrong irng voi vector m~u. Haitrng vien co d~c trung dirng d~u se diroc chon de tim kiem cho cac d~c trung khac cuakhuon m~t. Til cac d~c trung khong thay d6i, cac d~c trung khac se diroc xac dinh thongqua su danh gia xac xuit khoang each gifra cac d~c trung, han nfra plnrong sai cua phepthong ke nay co the duoc tinh toan tnroc de xac dinh cac d~c trung voi xac xuit 100. Ty l~chinh xac cua phirong phap nay la 86%.

Ben canh tinh khoang each lien quan de mo ta quan h~ gitra cac d~c trung nhuLeung. Kendall [4], Mardia and Dryden [12] dung ly thuyet xac suit thong ke vS hinhdang. DUng ham m~t dQ xac suit (Probility Density Function - PDF) qua N diem d~ctrung, tuong irng (xi, yi) la d~c trung tlnr ivai gia sir la phan b6 Gauss co 2N-chiSu. Cactac gia ap dung plnrong tlnrc eire dai kha nang (Maximum-Likelihood - ML) de xac dinhvi tri khuon m~t. MQt thuan IQ'icua phuong phap nay la cac khuon m~t bi che khuit v~nco the xac dinh duoc. Han chS plnrong phap nay do la khong xac dinh diroc nhiSu khuonm~t trong cung mot anh,

Khong dung plnrong phap xac xuit, Sirohey dua mQt phirong phap khac xac dinhkhuon m~t illmot anh co hinh nSn phirc tap [10], goi la plnrong phap dua tren canh. Ongdung phirong phap Candy [9] va heuristics loai be cac canh d~ con lai duy nhit mQtduong bao xung quanh khuon m~t. DUng mQt hinh ellipse de bao khuon m~t, tach bietvung d~u va hinh nSn. Ty l~ chinh xac cua thuat toan la 80%.

Graf dua ra mQt plnrong phap khac de xac dinh d~c trung, ill do xac dinh khuon m~ttrong anh xam [8]. DUng bQ lQCde lam n6i cac bien, cac phep toan hinh thai hoc(morphology) duoc dung de lam n6i b~t cac vung co cuong dQ cao va hinh dang chicchin (nhu mit). Thong qua histogram de tim cac dinh n6i b~t de xac dlnh cac ngu6ngchuyen anh xam thanh hai anh nhi phan. TiSp theo, ong xac dlnh cac (mg vien khuon m~tnha vao cac thanh phk dSu xuit hi~n trong ca hai anh nhi phan, sau do phan lo:;tixem cacung vien co phai la khuon m~t khong. Phuang phap duQ'ckiem tra va cho kSt qua t6t trencac anh chi co d~u va vai Clla nguai. Tuy nhien phuang phap nay con mQt vin dS diy laSlr d\ll1g cac phep toan morphology nhu thS nao va lam sao xac dlnh khuon m~t tren cacvimg (mg vien.

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1.2.4. Da d~ctrung.

Gk day co nhi8u nghien ciru sir dung cac d~c trung toan cue nhu: mau da nguoi,kich thucc, va hinh dang d tim cac Ung vien khuon m~t, r6i sau do se xac dinh Ung viennao la khuon m~t thong qua dUng cac d~c trung Cl,lC bQ (chi ti~t) nhu: m~t, long may, mill,mi~ng, va toc. Tuy m6i tac gia se sir dl,lllgt~p d~c trung khac nhau.

;l. ,1.2.3.Mau sac da.

Thong thuong cac anh mau khong xac dinh tnrc ti~p tren toan bQdfr lieu anh rna cactac gia dung tinh ch~t s~c mau cua da nguoi (khuon m~t nguoi) dS chon ra duoc cac irngvien co th la khuon m~t nguoi (luc nay dfr lieu dff thu hep dang kS) d xac dinh khuonm~t nguoi.

Park dung Gaze dS tim irng vien goc m~t, mieng va tam mit. Ong xay dung SVM(Support Vector Machine) dff duoc hoc tnroc do dS xac dinh cac vi tri irng vien co phai 1agoc m~t, mieng, va tam m~t hay khong dS theo v~t con m~t nguoi.

Juan va Narciso xay dung mdt khong gian mau moi YCg'Cr' dS loc cac vung la imgvien khuon m~t dna tren s~c thai cua mau da nguoi. Sau khi co img vien, hai ong dungcac quan h~ v8 hlnh dang khuon m~t, rmrc dQ can d6i cua cac thanh ph~n khuon m~t dSxac dinh khuon m~t ngiroi. Tuong tv, Chang va Hwang cling dung khong gian m~uYcg'Cr' voi tY l~ chinh xac hon 80% trong anh xam.

Jin xay dung mot bQ loc dS xac dinh irng vien khuon m~t nguoi theo mau da nguoi,Tir img vien nay tac gia xac dinh khuon m~t nguoi theo hlnh dang khuon m~t va cac quanh~ d~c trung v8 thanh phan khuon m~t, voi m~t phai diroc chon lam g6c toa dQ dS xetquan h~. Ty 1~chinh xac cho khuon m~t chup thang tren 80%.

1.2.2. K~tdu cua khudn m~t.Khuon m~t con nguoi co nhfrng k~t celu rieng biet rna co thS dung d phan loai so

voi cac d6i tirong khac. Tir k~t celu cua khuon m~t xay dung len mQt bQphan 10~i,goi 1ak~t c~u giong khuon m~t (face-like texture).

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1.3. Hurmg ti~p c~n d..,a tren so sanh khop m~u.

1.3.1. Tu tuong cua so sanh khop m~u

Trong huong tiep c~n dua tren so sanh khop m~u, cac m~u chuan cua khuon m~t(thuong la khuon m~t diroc chup thang) se diroc xac dinh truce ho~c diroc bi~u di~n thanhmQt ham voi cac tham s6 cu th~. Til anh dfiu vao, ta tinh cac gia tri tuong quan so voi cacm~u chuk v~ dirong vi~n khuon m~t, mit, mfii va mieng, Thong qua cac gia tri nrongquan nay rna h~ thong se quyet dinh co hay khong co t6n tai khuon m~t trong anh, Huongtiep c~n nay co loi thS la r~t d~ cai d~t, nhung khong hieu qua khi tY l~, nr thS, va hinhdang thay d6i.

1.3.2. Xac djnh cac m~u.

Sakai df:lc6 ging tlnr xac dinh khuon m~t nguoi chup thkg trong anh [20]. Ongdung vai m~u con (sub template) v~ mit, mfii, mieng, va dirong vi~n khuon m~t d~ mohinh hoa mQt khuon m~t. M6i m~u con duoc dinh nghia trong gioi han cua cac doanthang. Cac doan thang trong anh diroc trich bang each xem xet thay d6i cua h~ s6 goc vaso khop cac m~u con. Dfiu tien tim cac irng vien thong qua m6i nrong quan gifra cac anhcon va cac m~u v~ duong vien, Sau do, so khop voi cac m~u con khac. Hay noi mQt eachkhac, giai doan dfiu xem nhu la giai doan sa chS d~ tim img vien, giai doan thir hai la giaidoan tinh chS d~ xac dinh img vien co phai la khuon m~t hay khong. Y nrong nay dirocduy tri cho dSn cac nghien ciru sau nay.

Craw dua ra mQt plnrong phap xac dinh khuon m~t nguoi dua vao cac m~u v~ hinhdang cua cac anh duoc chup th~ng (dung ve b~ ngoai cua hinh dang khuon mat). Dfiu tiendung phep loc Sobel (Phep loc Sobel dua vao tich vo hu6ng cua hai vector gradient) d~tim cac c~. Cac c~nh nay se duQ'c nhom l~i theo mQt s6 rang buQc. Sau do, tim du

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d~ xac dinh khuon m~t nguoi. Phuong phap nay dinh nghia mQt s6 gia thuyet d~ mo ta cackha nang cua cac d~e trung khuon m~t. V&imQt khuon m~t se co mQt t~p gia thuyet, goila ly thuyet DepsterShafer. DUng mot nhan t6 tin e~y d~ kiem tra S\I t6n tai hay khong cuacac d~e trung cua khuon m~t, va k~t hQ'Pnhan t6 tin e~y nay voi mQt dQdo d~ xem xet cohay khong co khuon m~t trong anh.

Sinha dung mQt ~p nho cac anh bftt bi~n trong khong gian anh d~ mo ta khong giancac mau anh [15, 16]. Tu tuong ehinh cua ong dua vao S\I thay d6i mire dQ sang cua cacvung khac nhau cua khuon m~t (nhu hai mlit, hai rna, va tran), (quan h~ vS rmrc dQ sangcua cac vung con lai thay d6i khong dang k~). Sau dfty xac dinh cac c~p tY s6 cua mire dQsang cua mot s6 vung (mot vung t6i hon hay sang hon) cho ta mot hrong bftt bi~n khahieu qua. Ong luu S\I thay d6i dQ sang cua cac vung tren khuon m~t trong mot t~p thichhQ'Pvoi cac c~p quan h~ sang hon - t6i hon giira cac vung nho,MQt khuon m~t duoc xacdinh n~u no thoa man tftt ca cac c~p sang hon - t6i hon. Y nrong nay xuftt phat nr S\I khacbiet cua cuong dQgiira cac vung kS C\lC bQ tren anh, sau nay no diroc rna rong tren co sabi~n d6i wavelet d~ xac dinh nguoi di bo, xac dinh xe hoi, xac dinh khuon m~t. Y tuongcua Sinha con duoc ap dung trong cac nghien ciru vS thi giac cua robot. Hinh 3 cho thftymau khuon m~t voi 16 cung va 23 quan h~. Cac quan h~ nay duoc dung d~ phan loai, co11 quan M thiet y~u (cac mill ten mau den) va 12 quan M xac thuc (cac mill ten m~uxam). M6i mfii ten la mot quan h~. MQt quan h~ cua hai vung thoa man mau khuon m~tkhi tY l~ gifra hai vUng vuqt qua mQt ngu5ng va mQt khuon m~t duqc xac dinh khi co 23quan M th6a man diSu ki~n tren.

15

Hinh 3: MQtm[u khuon m~tM[u khuon m~t co kieh thuoc 14 x 16 pixel dirocsir dung trong phirong phap dinh vi khuon m~t cuaSinha. M[u g6m 16 vung tren khuon m~t va 23quan h~ ( cac mill ten).

MQt s5 phirong phap khac nhu cua Froba va Zink IQecanh 6 dQphan giai thftp r6idung bi&n d6i Hough d6 so khop m[u thea huang canh d6 xac dinh hinh dang khuon m~t6 dang chup hinh thang 6 dang xam. Ty l~ chinh xac tren 91%. Ngoai ra Shu va Jain conxay dung ngfr nghia khuon mat. Ngir nghia xay dung thea hinh dang va vi tri cac thanhphan khuon mat. Hai ong illbQngfr nghia nay tao mQt d6 thi quan h~ d6 d~ dang so khopkhi xac dinh khuon mat nguoi ...

1.3.3. Cac miu bi~n dang

Yuille dung cac m[u bien dang d6 mo hinh hoa cac d~c trung cua khuon m~t [3].Trong huang tip can nay, cac d~c trung khuon m~t diroc mo ta bing cac m[u duoc thams5 hoa va co mot ham nang luong (gia tri) duoc d6 lien kt cac canh, dinh, va thung lfingtrong anh dS nrong img voi cac tham s5 trong m[u. Md hinh nay t5t nhftt khi t5i thiSuham nang hrong qua cac tham s5, M~c du co kt qua t5t nhung phirong phap nay co mQts5 han ehB do la cac m[u bien dang phai duoc khoi tao trong pham vi gAn voi cac d5ituong rna no xac dinh.

Rftt nhiSu huang tip c~ dua tren duong gftp khuc (snake) va cac m[u dS xac dinhkhuon mat, DAu tien anh se duoc lam xo~ lai boi mQt IQe lam rna r6i dung phep toan

16

morphology dS lam n6i b~t canh len. Ti~p theo dung mot duong gap khuc co n diem anh(gia tri n nho) dS tim va uoc hrong cac doan cong nho, M6i khuon m~t duoc xap xi bangmQt ellipse va bien d6i Hough, r6i tim mot ellipse n6i trQi nhat. Cac img vien se co bontham s6 mo ta nQt ellipse (dS xac dinh khuon mat), V6i m6i irng vien, mQt phuong tlnrctuong tu nhu phirong tlnrc mftu bien dang duoc dung dS xac dinh cac d~c trung 6 nnrc chiti~t. N~u tim thay s6luQl1g dang kS cac d~c trung khuon m~t va thoa man ty l~ din d6i thixem nhir da xac dinh diroc mot khuon m~t. Lam va Yan cling dung duong gap khuc dSxac dinh vi tri dau voi thuat toan greedy dS cue tiSu hoa ham nang hrong [11].

Thay vi dung duong gap khuc thi Huang va Su [6] dung ly thuyet dong chay dS xacdinh duong vi~n khuon rn~t dua tren d~c tinh hinh hoc, Hai ong dung ly thuyet t~p hopd6ng mire (Level Set) dS loang ill cac khoi dong ban dau dS co duoc cac khuon m~tnguoi,

Lanitis mo ta rnQtphirong phap bieu di~n khuon m~t ngiroi voi ca hai thong tin: hinhdang va cuong dQ [12]. B~t dau bang cac ~p anh duoc huan luyen voi cac dirong vi~nmftu nhu la duong bao m~t, mill, c~m/ma da diroc gan nhan(phan loai). Ong dung mQtvector cac diSm mftu dS mo ta hinh dang. Sau do dung mQt mo hinh phan b6 diSm (pointDistribution Model- PDM) dS mo ta vector hinh dang qua toan bQcac ca thS. DUng eachtiep can nhu cua Kirby va Sirovich [14] dS mo ta cuong dQ b~ ngoai cua hinh dang dadiroc chuftn hoa. DS tim kiem va uoc hrong vi tri khuon m~t cling nlnr cac tham s6 v~hinh dang ong sir dung mot mo hinh PDM co hinh dang nlnr khuon m~t (xac dinh khuonm~t bang mo hinh hinh dang tich cvc - Active Shape Model - ASM). Cac manh cuakhuon m~t duQ'c lam bi~n d~ng v~ hinh dang trung binh r6i trich lay cac tham s6 cUOngdQ. Sau day cac tham s6 hinh dang va cUOng dQ dUQ'cdUng dS phan lo~i va xac dinhkhuonm~t.

1.4. DuOng ti~p e~n d1!a tren di~n m,o.

1.4.1. Tu tucYngella hUOng ti~p e~n d1!a tren di~n m,o

Trai nguQ'c v6i hu6ng ti~p c~ dva tren so sanh kh6p mftu (cac mftu da duQ'c cac nhanghien cUu dinh ngrua tru6c), cac mftu trong hu6ng ti~p c~n nay duQ'c hQc illmQt t~p anhmftu. Co thS noi hu6ng ti~p c~n dva tren di~n m~o ap d\lllg cac kY thu~t theo hu6ng xacsuat th6ng ke va hQcmay dS tim nhfrng d~c tinh lien quan cua khuon m~t va khong phai lakhuon m~t. Cac d~c tinh da duQ'chQc 6 trong hinh thai cac mo hinh phan b6 hay cac tharn

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a. Adaboost

Adaboost dU(Jcdanh gia la phuong ph

18

thea se diroc xay dung dua tren cac danh gia vS cac bQphan 10~iySu tnroc, cu6i cung cacbQphan 10~iySu se duoc kSt hQ'PdS tro thanh bQphan 10~imanh,

Viola va Jones dung AdaBoost kSt hQ'Pcascade dS xac dinh khuon m~t nguoi [17]voi cac d~c trung dang Haar-1ike. T6c dQ xu ly kha nhanh va tY 1~chinh xac hon 80%.Ngoai ra Schneiderman va Kanade dung wavelet dS trich d~c trung, sau d~y ding xaydung h~ thong hoc voi Adaboost, dua tren xac su~t dS xac dinh khuon m~t nguoi. Ty 1~chinh xac tren cua phirong phap nay len dSn 90%.

b. Mang Neural

V~ co ban mang neural la mQt mang cac phk ill (goi la neuron) kSt n6i voi nhauthong qua cac lien kSt (cac lien ket nay duoc goi la trong s6 lien ket) dS thuc hien mQtcong viec cu thS nao do. Kha nang xu ly cua mang neuron duoc hinh thanh thong qua quatrinh hieu chinh trong s6 lien kt giira cac neuron, noi each khac la hoc ill ~p hQ'Pcacmau huk luyen.

Mang neural r~t hay duoc su dung trong cac bai toan nhan dang mau, nhan dang m~tnguoi cling eo thS xem la mot bai toan nhan dang voi hai 10~imau (m~t nguoi hoac khongphai mat ngiroi) va nhu v~y co thS su dung mang neural kha hieu qua kS ca kill cac maukhuon m~t co dQphirc tap cao. Tuy nhien, mQt diSu tro ngai do la cac kien true mang d~uco tinh t6ng quat cao, do do, khi ap dung ta phai tinh toan r5 s6 hrong t~ng, s6 hrongnode, tY 1~hoc ... cho nrng tnrong hQ'Pcu thS.c. Support Vector Machine

Support Vector Machine (SVM) diroc Vladimir Vapnik dira ra vao nam 1995 dS giaiquyet v~n dS nhan dang mau hai lap su dung nguyen ~c cue tiSu hoa nii ro cAu true(Structural Risk Minimization). Day la plnrong phap tiep c~ phan 10~ivan ban r~t hieuqua. Vu diSm cua SVM la giai thuat nay duoc xay dung tren y nrong cue tiSu rui ro cAutrue. Nguon g6c cua SVM dVa tren sv ch~c ch~n vS 16i chinh xac, co thS phan lo~i ngaunhien cae mau d6i tuQ'llgduQ'c chQn rna 16i duQ'Cgiu sao cho nhc nh~t. Vi v~y, giai thu~tSVM giup giam thiSu bien tren cac 16ichinh xac va lam cho h~ th6ng tin c~y hon

Cho truac mQt t~p hu~n 1uy~n, cac anh duQ'c biSu di~n duai d~g vector. Trongkhong gian vector, m6i vertor duQ'c biSu di~n beri mQt diSm. Phuong phap SVM se timmQt sieu phkg quyet dinh dS phan chia khong gian vector thanh hai lap (khuon m~t va

19

khong phai khuon mat). Chat IUQ'Ilgcua sieu phang nay phu thuoc vao khoang each gifra

cac vector, nrc la phu thuoc vao cac d~c trnng cua anh,

Support Vector Machine d5:duoc Osuna [5] ap dung dftu tien (dS xac dinh khuonrn~t nguoi), SVM duoc xern nlnr la mot kieu phan loai moi vi trong khi hftu het cacphuong phap phan loai khac (nhu Mang Bayes, Nueral, RBF) dBu dung tieu chi t6i thieu16i huan luyen (rui ro do kinh nghiem), trong khi SVM dung quy nap (diroc goi la t6ithieu rui ro du true). Phan loai SVM la rnQtphan loai tuyen tinh, do do no cling dung rnQtsieu phang dS tach dfi lieu. Dua tren mot s6 k~t hop co cac trong s6 cua rnQt~p con nhocac vector huan luyen, cac vector nay diroc goi la support vector. Vac IUQ'Ilgsieu phangtrong SVM thi wong dirong giai rnQt bai toan tuyen tinh b~c hai. Osuna [5] d5:phat triSnphuong phap SVM rnQteach hieu qua cho bai toan xac dinh khuon rn~t ngiroi. Ong dung10,000,000 rnftu co kich thiroc 19x19 pixel, h~ thong cua ang co ty l~ 16i it hon va nhanhhon rat nhiBu.

d. Ma hinh Markov an

Mo hinh Markov An ( Hidden Markov Model - HMM ) la mot rna hinh thong ketrong do h~ thong duoc rna hinh hoa duoc cho la rnQt qua trinh Markov voi cac tharn s6khong bi~t tnroc va nhiem vu la xac dinh cac tham s6 An ill cac tharn s6 quan sat duoc,dua tren su thira nhan nay. Cac tharn s6 cua rna hinh diroc rut ra sau do co thS sir dung dSthirc hien cac phan tich k~ tiep, vi du cho cac irng dung nhan dang rnftu.

MQt gia thuyet quan trong cua rna hinh Markov ftn la cac rnftu co thS duoc d~c tinhhoa nlnr cac ti~n trinh ngftu nhien co tharn s6 va cac tharn s6 nay diroc uoc IUQ'Ilgchinhxac, day la mot trong nhfmg dinh nghia ro rang. Khi phat trien HMM dS giai quyet baitoan nhan dang rnftu, phai xac dinh ro co bao nhieu trang thai Andftu tien cho hinh thai rnahinh. Sau do, huan luyen HMM hoc xac suat chuyen ti~p gifra cac tr~ng thai ill cac rnftu,rna rn6i rnftu dugc rna ta nhu rnQt chu6i cac quan sat. M\lc tieu huan luy~n HMM la cvcd~i hoa xac suAt cua quan sat ill dfr li~u huAn luy~n b~ng cach diBu chinh cac tharn s6trong rna hinh HMM thang qua phuong phap phan do~n Viterbi chuftn va cac thu~t toanBaum- Welch. Sau khi huAn luy~n xong, dva vao xac suAt dS xac dinh rnQt quan sat thuaclap nao.

DS hinh dung vBHMM, ta xet vi d\l C\lthS sau: Gia sir A co rnQtnguai b~ B s6ng 6rAtxa. Hang ngay thi B dBu gQidi~n cho A dS kS vBnhUng vi~c rna B d5:lam trong ngay.

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mob 4: cac vector quan sat d~ hufro luyen cho HMM

w

,;p

B chi co 3 viec rna anh ta thich lam do la: viec tlnr nh~t la di dao, tlnr hai la di cho va tlnrba la don phong. Va viec anh ta lam viec gi trong ngay phu thuoc r~t 100 vao thai tiSt cuangay hom d~y. Nhu vay, du khong nhan duoc thong tin cu th~ v~ thai tiSt trong ngay 6noi nguoi B 6, nhimg nguoi A vftn co th~ dua vao viec B lam trong ngay d~ doan v~ thaitiSt hom d~y. Nhu v~y nSu coi thai tiSt chi co hai trang thai la nang va rmra thi thai tiSt lamdt chulh Markov cu the, va no la ~n d6i voi nguoi A. Dir 1i~uquan sat diroc 6 day laviec lam trong ngay cua nguoi B. Toan bQM thong nay la mQtmo hinh Mackov ~n.

MQt gia thuyet quan trong cua mo hinh Markov k la cac mftu co th~ duoc xem nhircac tiSn trinh ngftu nhien co tham s6 va cac tham s6 nay dtroc uoc luong chinh xac, Khiphat tri~n HMM d~ giai quyet bai toan nhan dang m&u, phai xac dinh ra co bao nhieutrang thai ~n d.u tien cho hinh thai mo hinh. Sau do, hu~n luyen HMM h9C xac su~tchuyen tiSp giira cac trang thai ill cac mftu, rna m6i m&uduoc mo ta nlnr mQt chu6i cacquan sat. Muc tieu hu~n luyen HMM la eire dai hoa xac su~t cua quan sat ill dfr lieu hu~nluyen bing each di~u chlnh cac tham s6 trong mo hinh HMM thong qua plnrong phapphan doan Viterbi chuan va cac thuat toan Baum- Welch. Sau khi hu~n luyen xong, diravao xac su~t d~ xac dinh mQt quan sat thuoc lap nao.

21

Hinh 6: Xac dinh khuon m~t bang HMMM6i trang thai lai co nhiing trang thai nho ben trong: trang thai tran

co ba trang thai nho ben trong; trang thai m~t co nam trang thai nhoben trong.

Thuong cac phuong phap dua vao HMM se xem xet mQt m~u khuon m~t nhu mQtchu6i cac vector quan sat, voi m6i vector la met day cac di~m anh (hinh 4 va hinh 6).Trong qua trinh hu~n luyen va ki~m tra, mQt anh duoc quet thee mQt thir tv va mot quansat duoc xem nhu mot kh6i cac di~m anh (hinh 4 va hinh 6). Sau d~y, ap dung mQt dinhhuang thee xac su~t d~ chuyen ill trang thai nay sang trang thai khac ( hinh 5 ), dfr lieuanh duoc mn hinh hoa bang phan b6 Gauss da bien. MQt chu6i quan sat bao gom cit cagia tri cuong dQ illm6i kh6i. K6t qua xuat ra cho bi~t quan sat thuoc lap nao.

mill

moh 5: Cac trang thai ~n

D6i voi bai toan phat hien va nhan dang m~t nguoi, ta chia khuon m~t thanh cacvung khac nhau nhu dAu,m~t, miii, mieng, va c~m. Co th~ nhan dang mQtm~u khuon m~tnguoi bang each thirc hien ti6n trinh xem xet cac vung quan sat theo mQt tlnr tv thich hop(illtren xuong diroi, ill trai qua phai). Muc tieu cua huang ti~p c~n nay la k~t hop cac

vung d~c trung khuon m~t voi cac trang thai cua mo hinh.

01

Oi+_ tran2 mat3 mw4 n~g5 cam

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d. Phan loai Bayes

Bayes la mQt bQphan loai tuyen tinh dira tren xac xu.t. Tu tuong cua no la dira vaoxac xu.t cua cac d~c trung xu.t hien trong khuon m~t. Day hi phuong phap don gian, caid~t khong plnrc tap, t6c dQ nhanh, voi t~p hu.n luyen Ian thi cho kSt qua van tuong d6ichinh xac,

MQt nghien ciru diSn hinh dung phan loai Bayes vao xac dinh m~t nguoi tronghuang tiSp c~n dna tren dien mao do Ii nghien ciru cua Schneiderman vi Kanade. Hai ongda mo ta mot phan loai Naive Bayes d&uoc IUQ'Ilgxac xu.t chung cua dien mao cue bQvivi tri cua cac mau (cac vung tren khuon mat) (1 nhieu dQ phan giai khac nhau. Ung voim6i dQ phan giai, khuon m~t nguoi duoc chia thanh b6n vung chfr nhat, cac vung nayduoc chieu xuong khong gian co s6 chiSu be hon vi diroc IUQ'Ilgillhoa thanh t~p cac mauco gioi han. Sau d.ythong ke cac vung da duoc chiSu d rna hoa dien mao cue bQ. NSuxac xuat Ian hon xac xu&tdat diroc Ian hon xac xu.ttiSn nghien thi kSt luan co khuon m~tnguoi. Huang tiSp c~n nay con cho phep xac dinh cac khuon m~t bi xoay hoac goc nhinnghieng.

MQt nghien ciru di~n hinh trong cac tiSp c~n nay do la cua Samaria. Samaria dadung nam trang thai tuong irng nam vung nhu trong hinh 6 d~ mo hinh hoa tiSn trinh xacdinh khuon m~t nguoi. Ong huan luyen nrng vung cho HMM. M6i trang thai se phu trachxem xet mQt vung tuong irng dS dua fa quyet dinh phu hop, NSu kSt qui xem xet cu6icung vuot qua mot ngufmg thi quan sat nay se Ia khuon m~t ngiroi.

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1.1. Ti~p c~n Boosting

V lich sir, boosting bit nguon ill cau hoi n6i tieng diroc dira ra boi Kearns vao nam1989 : "Lieu co th~ tao ra m9t strong classifier illm9t t~p cac b9 phan loai yeu?". Nam1990, Robert Schapire dira ra thuat toan boosting d~u tien, tiSp d&nnam 1993 thi no dirocDrucker, Schapire va Simard ki~m nghiem trong trong cac chuang trinh nhan dang (OCRapplication ). Freund dii tiSp tuc cac nghien ciru cua Schaprire, va d&nnam 1995 thi ongcung voi Schapire phat trien boosting thanh adaboost.

Nhtr vay, nguyen ly co ban cua boosting la S\I k&t hQ'Pcac weak classifiers thanhm9t strong classifier. Trong do, weak classifier la cac b9 phan loai dan gian chi dn co d9chinh xac tren 50%. Bang each nay, chung ta noi b9 phan loai dii duoc "boost".

D~ hieu each hoat dong cua thuat toan boosting, ta xet mot bai toan phan loai 2 lap(miu dn nhan dang chi thuoc mot trong hai lap) voi D la t~p huan luyen g6m co n miu.Tnroc tien, chung ta se chon ngiu nhien ra nl miu ill~p D (n1

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1.2. Adaboost

Nhu da biet, AdaBoost (Adaptive Boost) la mQt bQ phan loai manh phi tuyen phircdua tren hu6ng tiep c~n boosting duoc Freund va Schapire dua ra vao nam 1995.Adaboost hoat dong tren nguyen t~c kSt hQ'Ptuyen tinh cac weak classifier dua tren cacd~c trung Haar- Line d~ hinh thanh mot strong classifier.

Hinh 7: Boosting

'---------XI

0

'. 0 .., I ...

K~t hopcac bqphan loai

II~= 8'----------x, '--------- XI '--_....c_ x,

I I

phan I.-r-.:..---.!.--=.~..!.._loaiyen I I

11= 27

. .

... .... .. III ....... : ,. '.

.. ". ".... . , .

Tiep theo, chung ta se xay dung ~p D3 ill nhfrng mau kh6ng duoc phan loai t6t boisu ket hQ'Pgifra C1 va C2: nhfmg mau con lai trong D rna C1 va C2 cho kSt qua khacnhau. Nhu vay, D3 se g6m nhfrng mau rna C1 va C2 hoat dong khong hieu qua. Sau cung,chung ta se huan luyen bQphan loai C3 illD3.

Bay gio' chung ta da co mQt strong classifier: su kSt hQ'PC1, C2 va C3. Khi tiSn hanhnhan dang mQtmau X, kSt qua se diroc quyet dinh boi str thoa thuan cua 3 bQC1, C2 vaC3: NSu ca C1 va C2 dSu phan x vao cung mQt lap thi lap nay chinh la kSt qua phan loaicua X; ngiroc lai, nSu C1 va C2 phan X vao 2 lap khac nhau, C3 se quyet dinh X thuoc vSlap nao

25

Thu~t toan AdaBoost:

fk:gia tri cua d~c trung Haar-like

Pk: h~ s6 quyet dinh chi8u cua b~t phuong trinh

C6ng tlnrc tren c6 thS diSn giai nhu sau: NSu gia tri d~c trung cua m~u cho boi hamdanh gia cua bQphan loai vuot qua mQt nguong cho tnroc thi m~u d~y Ia khu6n m~t ( goila object: d6i tuong cftn nhan dang ), nguoc lai thi m~u la background (kh6ng phai Ia d6ituong ).

lh: ngirong

h (x"lo, =Iin.8uPkfii~) -c ?k~kP: ~ fl n~1J."W'yVr. I~;'

Trong d6:

x : m~u hay cira s6 con cftn xet (x = (XI,X2, ... ,xn) Ia vector d~c trung cua m~u)

DS e6 thS kSt hQ'Pcac bQphan loai ySu, adaboost su dung mot trong s6 (weight) dSdanh d~u cac m~u kh6 nhan dang. Trong qua trinh huan luyen, eu m6i weak classifierduoc xay dung, thuat toan se tien hanh e~p nhat lai trong s6 dS chuan bi eho viec xaydung weak classifier tiSp theo: tang trong s6 ella cac m~u bi nhan dang sai va giam trongs6 ella cac m~u diroc nhan dang dung boi weak classifier vira xay dung. Bing each nay,cac weak classifier sau e6 thS ~p trung vao cac m~u rna cac weak classifier tnroc d6 chuathuc hien t6t. Sau cung cac weak classifier se duoc kSt hQ'Pmy theo nnrc dQ 't6t' cuachung dS tao nen mQt strong classifier.

Cac weak classifiers hk(x) Ia cac bQphan loai ySu, duoc bieu diSn nhu sau:

26

4. Strong classifier duoc xay dung :

1 1-~1'""'r--i1n ( ff )'J

Zr: H~ s6 dung d~ dua m+l v doan [0,1] (normalization factor)

Trong do:

h{.'Til)= 'Yilhlo (;'If"{l) ;: )1p.

3. Xay dung T weak classifiersLap t = 1, ... , T

Voi m6i d~c trung trong vector d~c trung, xay dung mQt weakclassifier hj voi ngufmg 8jva 16iCj.

'If

~t- L Wr"fflh;tVr1i) - )CRIR

Chon ra hj voi Cj nho nh~t, ta duoc h.:h'f_: X -+ (1. -1)

C~p nhat lai trong s6:

{

-a'r:w6-,/t ,.. ,Wi.+ll,k = --;;-X at

""'f. V,

2. Khoi tao trong s6 ban dAu cho t~t ca cac m~u: voi m la s6 m~u dung(irng voi object va Y = 1) va tla s6 m~u sai (irng voi background va Y =-1).

1. Cho mQt t~p g6m n m~u co danh d~u (XI,yI), (X2,Y2), ... (xo,yo) voi Xk=(Xkl> Xk2, ... , Xlan) la vector d~c trung va YkE {-I, I} la nhan cua m~u(1 irng voi object, -1 irng voi background).

27

Trong cong thirc cua hi) phan loai H(x):,1' ,

H~) - daa,(I =, 14{x) ~\t=l )

Ta th~y t~t ca cac hi) phan loai h, d~u co dong gop vao k~t qua cua hi) phan loaiH(x), va rmrc di) dong gop cua chung phu thuoc vao gia tri

28

[]~~~(8) (b) (c) (d)

2. D~c trung duong (line features):

1. D~c trung canh (edge features):

Hinh 8 : 4 d~t trung Haar-like CCY ban

D~ ap dung cac d~t trung nay vao viec bai toan xac dinh m~t nguoi, 4 d~t trungHaar-like CCY ban duoc rna rong ra, va duoc chia lam 3 t~p d~c trung nhu sau:

I I I

1 1- tilQ(;~- ifn ( ~ )

1D~ thiy gia tri Ilt ti l~ nghich voi Ct. Boi vi h, diroc chon voi tieu chi dat Ctnho nhat, do do no se dam bao gia tri at 160nhat.

Sau khi tinh duoc gia tri Ut, Adaboost ti~n hanh c~p nhat lai trong s6 cua cac m&u:tang trong s6 cac m&u rna h, phan loai sai, giam trong s6 cac m&urna h, phan loai dung.B~ng each nay, trong s6 cua m&uphan anh duoc mire d9 kho nhan dang cua m&udo va h,+1 se uu tien hoc each phan loai nhfrng m&unay.

Vong l~p xay dung strong classifier se dirng lai sau T l~n l~p. Trong thuc t~ cai d~t(thu vien OpenCV cua Intel), nguoi ta it su dung gia tri T vi khong co cong thirc nao dambao tinh duoc gia tri T t6i uu cho qua trinh huin luyen. Thay vao do, nguoi ta su dung giatri max false positive hay max false alarm (ti l~ nhan dang sai t6i da cac m&ubackground). Ti l~ nay cua b9 phan loai ck xay dung khong duoc phep virot qua gia trinay. Khi do, qua cac l~n l~p, false alarm cua strong classifier Hjx) xiiy dung diroc (tai l~nl~p thir t) se giam d~n, va vong l~p k~t thuc khi ti l~ nay thap han max false alarm.

1.3. Caed~etrung Haar-Like

Viola va Jones dung 4 d~c trung CCY ban [20] d~ xac dinh khuon m~t ngiroi. M6i d~ctrung Haar-like la su k~t hop cua hai hay ba hinh chir nhat "trang'' hay "den" nhu tronghinh sau:

29

p(x,y)

f(x) =Tangvilngden(pixel) - TangvUngtdng(pixel)

Nhir vay ta co th~ thfty r~ng, d~ tinh cac gia tri cua d~c tnmg Haar-like, ta phai tinhtang cua cac vung pixel tren anh, Nhung d~ tinh toan cac gia tri cua cac d~c tnmg Haar-like cho cit ca cac vi tri tren anh doi hoi chi phi tinh toan kha 100, khong dap irng duoccho cac img dung doi hoi tinh run-time. Do do Viola va Jones dua ra mot khai niem goi laIntegral Image d~ tinh toan nhanh cho cac d~c tnmg C(J ban. Sau nay, Lienhart kS thiraIntegral Image (SAT) va dua them khai niem Rotated Summed Area Table (RSAT) dungd~ tinh toan nhanh cho cac d~c tnmg xoay I goc 45. Integral Image la mQt mang 2 chi~uvoi kich thuoc bang voi kich cua anh dn tinh cac d~c tnmg Haar-like, voi m6i phan tUcua mang nay duoc tinh bkg each tinh tang cua di~m anh phia tren (dong-I) va ben trai(cot-I) cua no. Bit d~u nr vi tri tren, ben trai dSn vi tri diroi, phai cua anh, viec tinh toannay dan thuan chi dua tren phep cong s6 nguyen dan gian, do do t6c dQ thuc hien rfttnhanh.

Hinh 9: Cac d~c tnmg rna rong cua cac d~c tnmg Haar-like C(J saLQ'i ich cua cac d~c tnmg Haar-like la no di~n dat diroc tri thirc v~ cac d6i nrong

trong anh (boi vi no bi~u di~n m6i lien h~ gifra cac bQphan cua d6i nrong), di~u rna banthan tirng di~m anh khong di~n dat duoc. D~ tinh gia tri cac d~c tnmg haar-like, ta tinh suchenh l~ch giira tang cua cac pixel cua cac vung den va cac vung trang nhu trong congtlnrc sau:

[!]~(a) (b)

3. D~c tnmg xung quanh tam (center-surround features):

30

p(;;,-y) - 2: J(xT,'/)p.,tml\~-""--I'l

Hinh 12: Vi du each tinh nhanh t6ng diSm anh ella vung D tren anh voi cac d~etrung xoay 45

Voi cac d~e trung Haar-like xoay 45 Integral Image tai mot dim (x, y) duoc tinhtheo cong three:

(b)(a)

4RSAT(x,y)

Hinh 11:Vi du each tinh nhanh t6ng cac dim anh ella vung D tren anh

A : B :________ -t,._'P..:,l__ -I' P2

C D

Hinh 10: Cach tinh Integral Image cua anh

Sau khi dll tinh diroc Integral Image, viec tinh t6ng diem anh cua mQt vung b~t kynao do tren anh tlnrc hien r~t don gian theo each sau: (Gia sir ta c~n tinh t6ng diSm anhcua vung D nhu trong hinh 11):

D=A + B + C +D- (A+B)-(A+C)+ A

Voi A + B + C + D chinh III gia tri tai diSm P4 tren Integral Image, nrong tu nhu v~yA+B III gia tri tai diSm P2, A+C III gia tri tai diem P3, va A III gia tri tai diSm Pl. V~y taco thS vi8t lai biSu thirc tinh D & tren nhu sau:

pt(~,~) - L i(Xf,;!)p.r~f~

31

1.4. Cascade of Classifiers

Ta thay qua trinh hu&u luyen, bQphan loai phai duyet qua tat ca cac d~c trung cuacac m~u trong t~p training. Vi~c nay t6n rat nhieu thai gian. Tuy nhien, trong cac m~u duavao, khong phai m~u nao cling thuoc loai kho nhan dang, co nhirng m~u background ratd~ nhan ra (ta goi day la nhfrng m~u background don gian). D6i voi nhfmg m~u nay, tachi cftn xet mQt hay vai d~c trung don gian hi co th~ nhan dien duoc chir khong c~n xet tatca cac d~c trung. Nhung d6i voi cac bQphan loai thong thuong thi cho du m~u dn nhandang la d~ hay kho thi no v~n se xet tat ca cac d~c trung rna no rut ra diroc trong qua trinhhoc. Do do, chung t6n thai gian xu 1)'-mot each khong dn thi8t.

Cascade of Classifiers ducc xay dung chinh la nhim rut ngan thai gian xu 1),-,giamthieu false alarm cho bQ phan loai. Cascade tree g6m nhieu stage (hay con goi la layer),m6i stage cua cay se la mot stage classifier. MQt m~u d~ duoc phan loai la d6i nrong thino dn phai di qua h8t cit ca cac stages cua cay. Cac stage classifiers 6 stage sau dirochuan luyen bing nhfrng m~u negative rna stage classifier tnroc no nhan dang sai, nrc la nose ~p trung hoc ti:r cac m~u background kho hon, do do su k8t hop cac stage classifiersnay lai se giup bQphan loai co false alarm thap. Voi cau true nay, nhfrng m~u backgroundd~ nhan dien se bi loai ngay ti:r nhfmg stages d~u tien, giup dap (eng t6t nhat d6i voi dQplnrc tap gia tang cua cac m~u dua vao, d6ng thai giup rut ng~n thai gian xu 1)'-.

Thu~t toan Cascade training:

Sum (D ) = .4 - 2 - 3 + 1

D = A + B + C + D - (A+B) - (A+C) + A

Nhu v~y t6ng cac di~m anh trong mQt hinh chir nhat (k~ ca tnrong hop xoay 45) batki dSu co th~ duoc tinh nhanh dna tren integral image tai 4 dinh cua no :

T6ng pixel cua mQt vung bat ky tren anh v~n diroc tinh thea each sau:

32

Input pattern classified as a non-object

falsealarms =~

h

2 NN-lstage 1

Minh hoa thu~t toan Cascade training:

Hinh 13: cascade of classifiers

Hinh 13 minh hoa su hufrn luyen cua mot cascade g6m N stages. o m6i stage, weakclassifier wong irng se duoc huan luyen sao cho dQchinh xac cua no la h va false alarmbing f.

N:= f/J Nu r,>Ftarget

N = { s6 m~u sai a stage hien tai phan loai sai }.P = { s6 m~u positive rna stage hien tai phan loai dung }

1. GQi:F la gia trifalse alarm va d la dQchinh xac cua weak classifier a m6i stageFtarget: Gia tri max false alarm.P,N la s6 hrong m~u positive va negative.Po N, la t~p positive va negative cho bQphan lap a tAng tlnr i.F;, Di: Gia tsi false alarm va dQchinh xac cua cascade truce khi d~n tAngthir i.

2. Khoi tao i=O;Fo=1.0; Do= 1.03. Lap: while Fi>Ftarget

i= i+1; Hufrn luyen bQphan loai hi ti:r t~p Pi va N,voi detection rate d va max

false alarmf Them hi vao cay phan lap. DUng cay phan lap hien co d~ tinh Fi: Duyet qua Nm~u negative cho

d~n khi nao tim du n m~u rna cay phan lap hien co phan loai sai.(if!I ~f.l"s --I .V'

33

Hinh 14: Cascade of boosting classifiers

Viola va Jones da sir dung r~t thanh cong cascade of boosting classifiers cho baitoan nhan dang mat ngiroi, Voi t~p hu~n luyen g6m 4196 hinh m~t nguoi diroc dua v~ anhgrayscale kich thuoc 24x24 va 9500 hinh background, hai ongda xay dung cftu truecascade tree g6m 38 stage voi t6ng cong 6060 d~c trung haar-like. Thuc nghiem da choth~y classifier a stage dftu tien sir dung 2 d~c trung va loai duoc khoang 50% m[ubackground (khong phai m~t nguoi) va co d

34

Hinh 15: ciu true C(J ban cua OpenCV

Phft.nCV bao g6m cac thu vien C(J ban v~ xu ly anh va cac giai thu~t v~ thi giac maytinh. ML la bl) thu vien v~ cac thuat toan hoc may, bao g6m rit nhieu bl) phan C\lID vaphan loai thong ke. HighGUI chua dung nhfmg thu tuc vao ra, cac clnrc nang v~ hru trfrcling nhir doc cac file anh va video. Phftn tlnr 4, Cxcore chua dung cac du true dfr lieu C(J

CV MLL HigbGUI

Cac ham v~ xu ly Cac thuat toan hoc Cac ham va thu tucanh va giai thuat v~ may, bao g6m cac bl) lam viec voi file anhthi giac may tinh phan C\lID, phan loai va file video

thong ke

rD II JLCXCORE

Cac ciu true dfr lieu C(J ban, du true XML, cac ham v~ d6hoa ...

Open VC h\ thu vien rna nguon rna cua intel v~ thi giac may tinh. No cung cip motbl) rna nguon bao g6m hang tram ham, lap dua tren cac thuat toan v~ xu ly anh cling nhuComputer vision dung ngon ngir CIC++. Open CV th~ hien SlJ da dang cua tri tue nhantao. Duoc irng dung nhieu trong cac bai toan nhan dang m~t, do tim mat, phat hien mat,IQcKalman, ...

ciu true t6ng quan cua OpenCV bao g6m 5 phdn chinh. 4 trong 5 phft.ndo duoc chira trong hinh ve diroi.

A1. SO' hrQ'cve OpenCV

Phat hien m~t nguoi trong anh co nhieu irng dung trong cuoc s6ng, tuy nhien, trongkhoa luan nay, t6i chi xay dung ml)t chuang trinh nho d~ minh hoa cho cac ly thuyet atren, Cu th~ diy la ml)t chuang trinh phat hien m~t nguoi trong anh diroc vi~t tren n~nvisual c ++ cua Microsoft, su dung thir vien rna nguon rna OpenCV cua Intel.

Chuong 4MY DuNG UNG DUNG

35

Nhiem vu chinh cua chuang trinh la do tim khuon m~t ill mQt birc anh, mQt filevideo hoac illweb cam, sau d~y hru khuon m~t vao csdl dBphuc vu cho cac muc dich khac(se duoc phat triBn sau).

3.1. Phan tich

Hinh 16: T6ng quan v h~ thong phat hien m~t nguoi trong anh

V co ban, ill anh g6c ban d~u, M thong se chia anh thanh vo s6 vung nho dB tinhcac d~c trung, sau d~y dira cac d~c trung. TiSp theo, chuang trinh se xac dinh cac vungkha quan (cac img vien) co thB la khuon mat, cu6i cung cac irng vien nay se diroc dira vaomot bQphan loai dBtien hanh xac dinh img vien nao la m~t nguoi.

3. Phan tich - thi~t k~ h~ thAng phat hi~n m,t nguot

Quy~t djnhk~tqua

Di~u ehinhthong s6AdaboostMQt t~pnho

cac d~etrung

S6Iuqng 100cac d~etrung

du truephan tAng(Cascade of classifier)

,goc

Integral ImageAnh

Tinhcac d~etrungnhanh ill nhi~u ti I~

Vae hrong cac d~etrung

ban ( vi du nhu d.u true XML, cac cay dfr lieu ... ). Ph~n cu6i cung la CvAux, phk naybao g6m cac thir vien cho viec phat hien, theo d5i va nhan dang d6i tuong (khuon m~t,m~t ... ).

2. TAngquan v~mQth~ thAngphat hi~n m,t ngU'Oitrong anh

36

X , I' d;\ ,a. U' y au vao:

Liru khuon m~t vao CSDL

Co khuon mat

Phat hien nhanhkhuon m~t bing

Adaboost

D~uvao(anh, video ho~c web cam)

Hinh 17: Bi~u d6 ngfr canh cua h~ thong

Nhir v~y cac chirc nang chuang trinh bao g6m:

KSt n6i dSn webcam, doc cac file anh va video.

Phat hien cac nhi8u khuon m~t co trong birc anh, video, webcam ...

Liru anh khuon m~t duoc phat hien vao csdl.

3.2. Thi~tk~h~thang

Voi cac clnrc nang (y tren, chuang trinh duoc chia thanh thanh 3 ph~n chinh: Phkxu 1y d~u vao, ph~n phat hien khuon m~t va phan xu 1y d~u fa.

Khuon m~tdii diroc phat hi~nI

HETHONGPHATH$N MAT NGVOr

TRONGANH

INGV0rDUNG

oCapture anh

37

b. Phat hi~nkhufmm~t

PhAn nay xu ly chinh nghiep V\1 cua h~ thong. Sau khi co anh truyen vao, h~ thongse thuc hien chirc nang phat hien khuon m~t co trong anh.

Viec phat hien khuon m~t duoc tlnrc hien nhanh bang thuat toan adaboost thong quaham cvHaarDetectObjectsO cua OpenCv' Ham nay thuc hien viec phat hien d6i nrongdua tren cac d~c trung haar-like, cu th la nho vao m9t b9 Cascade diroc truyen vao choham. B9 Cascade duoc xay dung thea dang cay (tree-node) va dff diroc huin luyen illtnroc.

Viec huan luyen b9 Cascade co th thuc hien ill nhirng dfr lieu thu th~p diroc dphuc V\1 cho qua trinh nhan dang, Vi du, mu6n nhan dang m9t nguoi A, ta thu thap cacanh khuon m~t cua nguoi A voi nhi~u nr th~, goc chup va di~u kien chup khac nhau, saudfty cho b9 nh~n dang hoc thea thuat toan Cascade training. Tuy nhien viec nhan dangkhuon mat la m9t cong viec kho va t6n nhieu thai gian, do do, toi khong tlnrc hien notrong khoa luan cua minh.

c. Xii' Iy dAu ra.

Khuon m~t sau khi diroc phat hien se duoc tach ra khoi birc anh va hru duoi danganh bitmap voi phAn rna rong la * .jpg.

U6i voi dAuvao la cac file anh, dfr lieu dAura cling la file anh dfty nhung co luu vitri khuon mat dff duoc phat hien tren birc anh, d6ng thai tach cac rieng khuon m~t va hruvao co sa dfr lieu.

U6i voi dAuvao la cac file video hoac webcam. Cac khuon m~t se duoc tach ra vahru l~i thanh cac file anh rieng re, va duQ'Cdanh s6 thir tv thea ten file video. Vi d\l nhufile video test.avi thi anh cac khuon m~t ph

38

Luu cac khuon m~t diroc phat hien

Nhir v~y cac thong tin trong co sa dfr lieu bao gom 2 thuc th~ sau:

A.NH G6C ( IMAGES ): Anh diu vao.

Cac thuoc tinh bao gom:

ID: Ma anh, kieu Integer, la khoa chinh va co rang buoc la tv tang.

Hinh 18: Bi~u d6 phan ra clnrc nang.

3.3. Thi~t k~ CO' SO' dfr li~u

Luu trfr co sa dfr lieu la mQtphin quan trong cua irng dung. Viec xay dung, t6 clnrcco sa dfr lieu anh lnrong 100 dn t6c dQcua chuang trinh. Do do toi da xay dung va quanco sa dfr lieu b~ng M quan tri co sa Oracle, day la h~ quan tri co sa dfr lieu manh, dapirng duoc nhieu yeu cftu vS quan 1yco sa dfr lieu cua cac doanh nghiep 100, d~c bi~t 1acacngan hang hay kho bac. Tuy hien tai, chuang trinh chi dirng lai a mire phat hien khuonm~t trong anh, nhung viec xay dung mQt co sa dir lieu tM se la tiSn dS d co th phat trienchuang trinh thanh cac irng dung 100han sau nay.

H~ thong cin 1uu trfr cac dfr lieu: Anh khuon m~t sau khi da duoc phat hien.D6i voi diu vao la file anh:

Luu anh diu vao cling vi tri khuon m~t da duoc phat hien tren birc anh

Luu nrng khuon m~t duoc phat hien,

D6i voi diu vao la file video, hoac webeam:

HE THONG pHATHIENMAT NGTJOI TRONG ANH

I~ ~ ~-1 I.Xu ly dAu vao I 2.Pbat hien nhanh 3. Xu ly dAu ra

khuon m~t bingAdaboost

1.1. Capture anh I 3.1. Luu anh vao thu muc- thich hQ"Ptren may

y 3.2.Luu anh vao CSDL

39

3.4. Tbi~tk~giaodi~n

Giao dien cua chuang trinh duoc thi~t k~ than thien va dan gian. T~p trung van clnrcnang chinh phat hien khuon m~t trong anh. B6 C\lC tren giao dien duoc chia thanh 4 phan.

Hinh 19: Sa d6 thuc thS quan h~ (ERM).

Image_ID

- Name: Ten cua birc anh, kieu dft lieu varchar2(200). Trong tnrong hopdAuvan la video thi ten se duoc lAytheo ten cua file video, con n~u dAuvan la webcam thi se d~t la webcam_ID (them ID (1 tren van ten).

- Image: Luu rna nhi phan cua birc anh.- URL: luu duong dfuI d~n birc anh, kieu gift lieu varchar2(1000)

Luu y: Trong tnrong hop file dAuvan la video hoac webcam thi cac tnrongImage, va URL se nhan gia tri null.

KHUON ~T (FACES ): Cac khuon m~t phat hien duoc.Cac thuoc tinh bao g6m:

- ID: Ma anh, kieu Integer, Ia khoa chinh va co rang bU9C Ia tu tang.- IMAGE_ID: ID cua anh g6c, ki~u dft lieu Interger, la khoa ngoai tham

chieu d~n bang ANII G6c.- Name: Ten cua khuon m~t, duoc d~t thea ten anh g6c, d6ng thai them s6

tlnr tu d~ nhan bi~t cac khuon m~t cua cung anh g6c.- Image: Luu rna nhi phan cua anh khuon m~t.- URL: Iuu duong dan d~n anh khuon m~t, kieu gift lieu varchar2(1 000).

40

Hinh 20: Giao dien cua chuang trinh

Tme to IU'I: 62.078618 ms

Detect I Stop I ~,Information

Num of Face: 1

lMG_3964.jpg

Faces in database

.----Image video webcam --...,load Image I load Video I~Duy.JPGlMG 3743:

Phn 1, danh cho nguoi dung chon du vao, co th~ la anh, video boac webcam. Cac anhhoac video co th~ duoc 1uu my y trong cac thu m\1Ctren may tinh cua ngiroi dung, Ph~nthir hai la danh sach cac khuon m~t dll duoc phat hien va diroc hru trong co sa dfr lieu.Ph~n tlnr 3 la danh sach cac Cascade. Cu6i cung la phan xu 1y va hien kSt qua (s6 khuonm~t phat hien duoc, thai gian thuc hien), Nguoi dung chon birc anh, chon bQCascade, sauday nhan vao nut "Detect" d~ tlnrc hien viec xac dinh khuon m~t tren anh, Giao dien cuachuang trinh duoc th~ hien nhu hinh duoi .

41

Tren co sa nghien ciru v~ bai toan phat hien m~t ngiroi trong anh. D~c biet la thuattoan Adaboost, cac d~c trung haar-like va mo hinh Cascade of Classifier. Toi da ap dungthanh cong mo hinh Cascade of Classfier vao bai toan phat hien m~t nguoi trong anh. TuykSt qua dat duoc chua cao, nhung day la buoc d~u dS phat triSn bai toan nhan dang m~t(face recognition) nguoi sau nay. Ben canh do, trong qua trinh xay dung chuong trinhdemo, toi da tim hieu v~ thu vien rna nguon rna OpenCV, M qulin hi co sa dfr lieuOracle, qua do biSt each sir dung cac ham trong thu vien OpenCV va quan ly co sa dfrlieu bing h~ quan hi co sa dfr lieu Oracle.

V~ clnrong trinh demo, sau khi tlnr nghiem voi t~p anh Ian va webcam. Chuongtrinh dat kt qua t6t, thai gian phat hien nhanh d6i voi cac anh m~t nguoi chup thing, chatluong anh t6t (chup bing may ki thudt s6), tuy nhien d6i voi cac anh chup nghieng hayanh co m~u sic qua t6i thi chuong trinh g~n nhu khong thS phat hien diroc khuon m~ttrong birc anh dfiy.

D6i voi webcam, thi hieu xufit cua chuong trinh con my theo chfit hrong cua Ioaiwebcam duoc sir dung, tuy nhien cling nrong tu nhu voi phat hien khuon m~t trong anh,chuong trinh se khong thS phat hien m~t nguoi trong di~u kien dO sang qua ySu va gocquay nghieng qua 200

2. Huang phit triSn

Co nhieu huang phat triSn cho chirong trinh nay, co thS phat triSn ca v~ m~t imgdung va m~t thuat toan (dS cai thien hieu qua phat hien m~t ngiroi). Co thS xay dung mOtirng dung chi cAn dSn phat hien m~t nguoi rna khong cAn nhan dang. Vi du nhu mOt h~thong ghep hinh, ghep khuon m~t phat hien duoc vao trong mOt birc anh khac (chang hannhu ghep khuon m~t cua nguoi sir dung cho khuon m~t cua nguoi nbi tieng),

Ngoai ra co thS phat trien chuong trinh theo huang nhan dang khuon mat, xay dungmot h~ thong dS hQc cac d~c trung cua nhUng nguai ck nh~ d~g. Khi th\lc hi~n, d~utien ta dua qua buc anh qua chuO'llg trinh phat hi~n m~t nguai dS phat hi~n nhanh cackhuon m~t co trong anh, sau dfiy so sach cac khuon m~t do v&i cac khuon m~t rna chUO'llg

1. KSt luan

KET LuANVAHUONGPHAT TIDEN.

42

trinh dffdiroc "hoc" tl:r wac, so sanh cac d~c trung cua hai khuon m~t, n@utrung thi duafa thong tin v~ khuon m~t duoc nhan dang.

b[10] 1. Sobottka and I. Pitas, "Segmentation and Tracking of Faces in Color Images",Proc. Second Int'l Conf. Automatic Face and Gesture Recognition, pp. 236-241,1996.

[9] 1. Canny, "A Computational Approach to Edge Detection", IEEE Trans. PatternAnalysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, June 1986.

[8] H.P. Graf, T. Chen, E. Petajan, and E. Cosatto, "Locating Faces and Facial Parts",Proc. First Int'l Workshop Automatic Face and Gesture Recognition, pp. 41-46,1995.

[7] G. Yang and T. S. Huang, "Human Face Detection in Complex Background",Pattern Recognition, vol. 27, no. 1, pp. 53-63, 1994.

[6] Fuzhen Huang and Jianbo Su, "Multiple Face Contour Detection Using adaptiveFlows", Sinobiometrics 2004, LNCS 3338, pp. 137-143, Springer-Verlag Berlineidelberg,2004.

[5] E. Osuna, R. Freund, and F. Girosi, "Training Support Vector Machines: AnApplication to Face Detection" Proc. IEEE Conf. Computer Vision and PatternRecognition, pp. 130-136, 1997.

[4] D.G. Kendall, "Shape Manifolds, Procrustean Metrics, and Complex ProjectiveShapes", Bull. London Math. Soc., vol. 16, pp. 81-121, 1984.

[3] A. Yuille, P. Hallinan, and D. Cohen, "Feature Extraction from Faces Usingeformable Templates", Int'l1. Computer Vision, vol. 8, no. 2, pp. 99-111, 1992.

[2] Andrew King, "A Survey of Methods for Face Detection", 2003

[1] A. Lanitis, C.J. Taylor, and T.F. Cootes, "An Automatic Face Identification SystemUsing lexible Appearance Models", Image and Vision Computing, vol. 13, no. 5, pp.393-401, 1995.

Tai lieu tham khao ..

c[21] Robin Hewitt, "Seeingwith OpenCV:A Five-Part Series", 2007

[20] T. Sakai, M. Nagao, and S. Fujibayashi, "Line Extraction and Pattern etection in aPhotograph", Pattern Recognition, vol. 1,pp. 233-248, 1969.

[19] T.K. Leung, M.C. Burl, and P. Perona, "Finding Faces in Cluttered Scenes UsingRandom Labeled Graph Matching", Proc. Fifth IEEE Int'l Conf. Computer Vision,pp. 637-644, 1995.

[18] T.F. Cootes and C.l Taylor, "Locating Faces Using Statistical Feature Detectors",Proc. Second Int'l Conf. Automatic Face and Gesture Recognition, pp. 204-209,1996.

[17] P. Viola and M. l Jones, "Robust Real-Time Face Detection", International Journalof Computer Vision 57(2), 137-154, Kluwer Academic Publishers, Netherlands,2004.

[16] P. Sinha, "Processing and Recognizing 3D Forms," PhD thesis, Massachusetts Inst.of Technology, 1995.

[15] P. Sinha, "Object Recognition via Image Invariants: A Case Study", InvestigativeOphthalmology andVisual Science,vol. 35, no. 4, pp. 1735-1740, 1994.

[14] M. Kirby and L. Sirovich, "Application of the Karhunen-LoeveProcedure for theCharacterization of Human Faces", IEEE Trans. Pattern Analysis and MachineIntelligence, vol. 12, no. 1,pp. 103-108,Jan. 1990

[13] Ming-Hsuan Yang, David Kriegman, and Narendra Ahuja, "Detecting Faces inImages: A Survey", IEEE Transactions on Pattern Analysis and MachineIntelligence (PAMI), vol. 24, no. 1,pp. 34-58,2002.

[12] K.V. Mardia and I.L. Dryden, "Shape Distributions for Landmark Data", AdvancedApplied Probability, vol. 21, pp. 742-755, 1989.

[11] K. Lam and H. Yan, "Fast Algorithm for Locating Head Boundaries," J.ElectronicImaging, vol. 3, no. 4, pp. 351-359, 1994.