Automated Face Detection and Recognition

Automated Face Detection and Recognition A Survey Waldir Pimenta [email_address] Universidade do Minho Mestrado em Informática MI-STAR 2010

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 a room (e.g. for temperature adjustment)

Face Recognition: applications Biometrics / access control ""Minority Report" ©2002 20th Century Fox Superbad" ©2007 Columbia Pictures Searching mugshot databases Tagging photo albums Detecting fake ID cards no action required scan many people at once places: airports, banks, safes data: laptops, medical info

Humans vs. Computers "Built-in" face detection / recognition ability detection & recognition in different areas of the brain can be fooled by look-alikes © SingularityHub.com Algorithms must be built from scratch Virtually perfect memory Can work 24/7 without degrading performance Can apply stricter matching criteria

Computer representation of faces Faces vary across many attributes — they're multidimensional Plotted in spaces with more than 3 dimensions in fact, it's commonly one dimension per pixel on a 20×20px image, that's 400 dimensions! Humans can't visualize or compute distances intuitively in >3D space. Computers can. But... It is computationally intensive. Dimensionality reduction is applied to enhance efficiency

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 <cnx.org/content/m11461/>

What defines a "match"? Ideally, distance in "facespace" should be: zero, for a specific match in face recognition small, for a generic face large, otherwise But there are variations due to: facial expressions illumination variance pose (orientation) dimensionality reduction

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.

Some history... Francis Galton (1888) Designed a biometric system for description and 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) Visual identiﬁcation of people by computer Takeo Kanade (1973) Computer recognition of human faces

Classification Zhao 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 × recognition pre-processing 3D Video

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

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 back to the original pixel–based representation, many producing face-like images (hence the name eigenfaces ) © AT&T Laboratories

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 Belhumeur et al. Shashua [1994] demonstrated that images from same face but under different illumination conditions lie close to each other in the high- dimensional facespace LDA can grasp these similarities better than PCA, which makes Fisherfaces more illumination independent than eigenfaces

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 <commons.wikimedia.org>

Gabor wavelets First proposed in 1968 by Dennis Gabor Analog to Fourier series: images are decomposed in a series of wavelets applied in different points Further developed to flexible models: elastic grid matching. GFDL Wikimedia Commons © Wiskott et al. 1997

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 2D deal poorly with varying poses (orientation) of the head Many have attempted to compensate by storing several views per face obviously resource-consuming 3D attempts to solve this issue, using: ©2006 Bowyer et 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

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 relative movement of tracked points. This is called Structure from Motion (SfM) ©2010 Christian Rakete <http://www.dorfpunks.de>

Comparison Standard tests needed for valid results comparison Databases: FERET, MIT, Yale, and many smaller ones Evaluations: 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)

Automated Face Detection and Recognition

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