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Automated Face Detection

and RecognitionA Survey

Waldir Pimentawaldir@email.com

Universidade do MinhoMestrado em Informtica

MI-STAR 2010

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Face Detection

Locating generic faces in images

2009 Angelo State University

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Face Detection: applications

Web cams that track the user Cameras that shoot automatically when they

detect smiles Blurring of faces in

public image databases

2009 Google

Counting of people in aroom (e.g. fortemperature adjustment)

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Face Recognition

Distinguishing a specific face from other faces

2009 TotallyLooksLike.com

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Face Recognition: applications

Biometrics / access control

""Minority Report" 2002 20th Century Fox

Superbad" 2007 Columbia Pictures

Searching mugshotdatabases

Tagging photo albums Detecting fake ID cards

o no action requiredo scan many people at onceo places: airports, banks, safeso data: laptops, medical info

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Humans vs. Computers

"Built-in" face detection /recognition ability

detection &recognition in

different areasof the brain can be fooled

by look-alikes

SingularityHub.com

Algorithms must bebuilt from scratch Virtually perfect

memory

Can work 24/7without degradingperformance

Can apply stricter

matching criteria

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Computer representation of faces

Faces vary across many attributes they'remultidimensional

Plotted in spaces with more than 3 dimensionso in fact, it's commonly one dimension per pixelo on a 2020px image, that's 400 dimensions!

Humans can't visualize or compute distancesintuitively in >3D space. Computers can. But...

It is computationally intensive. Dimensionality

reduction is applied to enhance efficiency

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PCA: Principal component analysis

Data is projected into a lower dimensional space preserving the directions that are most significant not necessarily orthogonal to the original ones!

cc-by Lydia E. Kavraki

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What defines a "match"?

Ideally, distance in "facespace" should be:o zero, for a specific match in face recognitiono small, for a generic faceo large, otherwise

But there are variations due to:o facial expressionso illumination varianceo pose (orientation)o dimensionality reduction

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The distance theshold

faces closer to each other than a given limit(threshold) are considered matches.

A looser threshold can be used for facedetection.

1991 M. Turk and A. Pentland

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The ROC curve

Too low threshold = more false negatives Too high threshold = more false positives

EER = Equal error rate

2007 Y. Du and C.-I. Chang"Handbook of Fingerprint Recognition" 2004 D. Maltoni et al.

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Some history...

Francis Galton (1888)Designed a biometric system for descriptionand identification of faces

2007 University of Texas at Austin

Public Domain

Woody Bledsoe (1964)First implementation of automatic facial

recognition in a mug shot database.

Michael D. Kelly (1970)o Visual identification of people by computer

Takeo Kanade (1973)o Computer recognition of human faces

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ClassificationZhao et al., 2003:[The facial recognition problem has] attracted researchers from very

diverse backgrounds: psychology, pattern recognition, neural

networks, computer vision, and computer graphics.geometric (feature based) photometric (image based)

detection recognitionpre-processing

3DVideo

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Pre-processing Face location / normalization Later processing doesn't need to scan the whole image Morphological operators (very fast) Rough operators to detect heads Finer confirmation operators to detect prominent features

Brunelli and Poggio 1993 Reisfeld et al., 1995

f

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Eigenfaces Sirovich and Kirby 1987; Turk and Pentland 1991 Uses PCA to discover principal components (eigenvectors) Each face is described as a linear combination of the main

eigenvectors Image-based approach

(features might not be intuitive)

eigenvectors can be translated backto the original pixelbasedrepresentation, many producingface-like images (hence the nameeigenfaces)

AT&T Laboratories

Fi h f

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Fisherfaces Instead of PCA, it uses Linear disciminant analysis (LDA),

developed by Robert Fisher in 1936 Variation can be greater due to lighting than due to different

faces (Moses el al. 1994)

1997 Belhumeuret al.

Shashua [1994] demonstrated thatimages from same face but underdifferent illumination conditionslie close to each other in the high-dimensional facespace

LDA can grasp these similarities better than PCA, whichmakes Fisherfaces more illumination independent thaneigenfaces

N l t k

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Neural networks Based on the natural brain structure

of simple, interconnected neurons Good at approximating complex prob-

lems without deterministic solutions Each pixel of the face image is mapped to an input neuron The intermediate (hidden-layer) neurons are as many as the

number of reduced dimensions that are intended. The network learns what patterns are likely faces or not Initially promising, but Cottrell and Fleming [1990] showed

that they can at best match an eigenface approach.

cc-by-sa Cburnett

G b l t

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Gabor wavelets

First proposed in 1968 by Dennis Gabor

Analog to Fourier series: images are decomposed in a seriesof wavelets applied in different points Further developed to flexible models: elastic grid matching.

GFDL Wikimedia Commons

Wiskott et al. 1997

A ti Sh /A M d l

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Active Shape/Appearance Models Original concept by Kass et al., 1987: snakes, deformable

curves that adjust to edges Yuille [1987] extended the concept to flexible sets of

geometrically related points (not necessarily on a curve) Cootes [2001] applies statistical analysis to model and

restrict the variation (flexibility) of model points

2001 Cootes et al.

3D

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3D 2D deal poorly with varying poses

(orientation) of the head

Many have attempted to compensate bystoring several views per faceo obviously resource-consuming

3D attempts to solve this issue, using:

2006 Bowyeret al.

active range sensors (laser scanners, ultrasound)

passive sensors (structured light: grid projected on face) New poses can be matched by deforming the 3D model

Vid

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Video Lower quality images (frames), due to compression.

Reconstructed models will have low accuracy. Advantage: temporal coherence, optical flow Simplest approach: use frame difference to detect moving

foreground objects and match their shapes (blobs) to heads Locate faces, then track them

Reconstruct 3D shape from relativemovement of tracked points. This iscalled Structure from Motion (SfM)

2010 Christian Rakete

Comparison

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ComparisonStandard tests needed for valid results comparison

Databases: FERET, MIT, Yale, and many smaller onesEvaluations: Face Recognition Vendor Test (FVRT) Face Recognition Grand Challenge

XM2VTS

Conferences: International Conference in Audio- and Video-Based Person

Authentication (AVBPA) International Conference in Automatic Face and Gesture

Recognition (AFGR)

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Questions?