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Local descriptors and similarity measures for frontal face recognition: A comparative analysis

About the Author: Witold Pedrycz Department of Electrical and Computer Engineering, University of Alberta, CanadaProfessor & Canada Research Chair & IEEE Fellow & Professional Engineer

Research interests and activities: Software Engineering System modelling and knowledge discovery Reconfigurable and evolvable architectures. Pattern recognitionPersonal Homepagehttp://www.ece.ualberta.ca/~pedrycz/index.html

About the Author: Marek Reformat A member of the IEEE and ACM.A member of program committees of several conferences related to computational intelligence and software engineering. Actively involved in North American Fuzzy Information Processing Society (NAFIPS).Research interests and activities: Knowledge extraction and knowledge representation Semantic-based intelligent systems Decision support Software quality and maintenancePersonal Homepage http://www.ece.ualberta.ca/~reform/index.html

Contents Main process Local descriptors Gabor filter with LBP Similarity measures & Experimental results

TaxonomyOn pixelLBP,CS,RIPsychological WLDTernary LTP,DLTPDistance basedTPLBP,FPLBP

Local descriptorsRotation invarShifted LBP

Selete subsetU2,DLBP,SELBPOn averagedILBP,MBLBP,GRAB

Three DimenVLBP,LBP-TOP

MultiresolutionGabor,MB,GRABlocal descriptors on Gabor filtered imageGabor magnitudeLGBP,MHLVP,LGBPHS,MULGBPGabor magnitude & phaseELGBP,MBP3DGV-LBPTOPGabor phase quantizationHGPP,LGPDP,LGXPDerivative patternsELGBP,LDP

Main process12 3

Main process231143981299782165Local pattern:pixel level description

Contents Main process Local descriptors Gabor filter with LBP Similarity measures & Experimental results

Local binary patterns:Circular LBP

Bilinear interpolation of a pixelBasic LBP57

Uniform-Ri LBPBAElongated LBPNew Variants Dominant LBPStatistically Effective LBPHamming LBPBaisc LBP etc

Local binary patterns:Threshold100.11

ILBP

Local binary patterns:

Threshold

L2L3L4

MagnitudeL1

SignELBP

Local binary patterns:MagnitudeSign

Original imageCenter gray levelLocal differenceMSclbp_Sclbp_Mclbp_Cclbp_mapclassifierclbp_HistogramCLBP

Local binary patterns:231129812142321610-11011-1DLTP=| LTPU-LTPL|=135-40=951001011000100001LTP5(AELTP)LTP

Local binary patterns:Soft-LBP

Gabor filter with LBP:SILTP[64(1-t) 64(1+t)]0000100010000101t=0.1

Local binary patterns:

Integral image

D=4+1-(2+3)MB-LBP

Local binary patterns:GRAB(General Region Assigned to binary)

1238476512436578 5GARB#noise & variations & rotation tolerant operatorsolving the orientation problemsmall variation in edge angles cause smaller variations in the binary representation

Local binary patterns:

CS-LBP

Local binary patterns:

TP-LBP

Local binary patterns:

FP-LBP

Local binary patterns:LDP

85322653501060384531397503537039916197161001110001Robust against Gaussian white noise and non-monotonic illumination changes 2Rotation invariant

Local binary patterns:

VLBP

Local binary patterns:

VLBP

Local binary patterns:LBP-TOP

Local binary patterns:LBP-TOP

Local binary patterns:LBP-TOP

Local binary patterns:LBP-TOP

Contents Main process Local descriptors Gabor filter with LBP Similarity measures & Experimental results

Gabor filter with LBP

Gabor filter with LBP

==++

Gabor filter with LBPGaborDennis Gabor, 194633

Gabor filter with LBP:

10 GGPP80 GGPPHGPP

Gabor filter with LBP:

GGPPLGPP110011010HGPP

Gabor filter with LBP:LGPDP

Gabor filter with LBP:LGXP

Contents Main process Local descriptors Gabor filter with LBP Similarity measures & Experimental results

Similarity measures

Fusing sub-region1: All sub-regional histograms concatenated2: Sub-regions of two images are compared pair-wise and the results are aggregated

Fusing sub-regionI2I1I1I2LGXPLGBP_magA:Feature level fusion

Fusing sub-region

LGBP_magI1I2I2I1LGXPOthers measure:1:cosine distance measure2:LDA1: AdaBoost2: Borda countReduction dimensionality

Thank you2013-04-1613113.4232013-04-1613165.667