Facial Recognition By Lisa Tomko

Overview What is facial recognition? History The Face Recognition Technology Evaluation The Face Recognition Vender Test Facial Recognition Grand Challenge Principle Components Analysis Linear Description Analysis Elastic Bunch Graph Matching Applications Research

What is facial recognition? Given a still or video image of a scene, Identify or verify one or more persons in the scene using a stored database of faces

A Brief History 1960’s First semi-automated system Programs designed by Woody Bledsoe, Helen Chan Wolf and Charles Bisson. 1988 Linear algebra technique implemented Less than 100 values needed to align and normalize a face. 1991 Turk and Pentland Real time automated face recognition Implemented in 2001 super bowl

FERET 1993-1997 The Face Recognition Technology Evaluation Sponsored by Defense Advances Research Products Agency Encouraged development of face recognition algorithms

FRVT 2000, 2002, and 2006 The Face Recognition Vender Test Evaluate work of FERET Assess commercial facial recognition Educate public In 2002- 90% verification and 1% false accept rates

FRGC Facial Recognition Grand Challenge Evaluated the latest in face recognition algorithms Used: High Resolution photos 3D Face Scans Iris images 10x more effective then 2002 100x more effective than 1995

PCA Pioneered by Kirby and Sirivich in 1988 Images must all be the same size and normalized Uses Data compression to reduce the detentions of the data and removes information that is not useful Decomposes facial structure into orthogonal components known as eigenfaces, stored in a 1D array Pro: Only needs 1/1000 of data presented Con: Needs full frontal face

LDA Each face is represented by a large number of pixel values Used to reduce number of features to a more manageable number before classification Fishers Faces Maximize between-class variance Minimize within-class variance

EBGM Relies on concept of nonlinear features Lighting Pose Expression Creates a dynamic link architecture that projects the face onto a grid Garbor jet is a node which describes image behaviors around pixels Garbor filter extracts shapes and detects features Accurate land mark localization is needed

Applications Law enforcement Facial recognition using various databases Mobile applications SocialCamera SceneTap FaceR Celebrity Entertainment TV set top box

Areas of Research MIT-multidimensional morphable models, view-based human face detection, cortex-like mechanisms, and object detection by components. MSU-data clustering, statistical pattern recognition, face detection in color images, the use of faces and fingerprints for personal identification, and kernel principal component analysis. UCSD- use of shape contexts for object recognition, slow feature analysis, classifying facial actions, and face recognition using independent component analysis.

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