M.I.T Media Lab oratory Perceptual Computing Section Technical Rep ort No. 443 App ears in: The 3rd IEEE Int'l ConferenceonAutomatic Face & GestureRecognition, Nara, Japan, April 1998 Beyond Eigenfaces: Probabilistic Matching for Face Recognition Baback Moghaddam, Wasiuddin Wahid and Alex Pentland MIT Media Lab oratory, 20 Ames St., Cambridge, MA 02139, USA. Email: fbaback,wasi,
[email protected] facial expressions of the same individual ) and extrapersonal Abstract variations (corresp onding to variations b etween di er- E We prop ose a novel technique for direct visual ent individu al s). Our similarity measure is then expressed matching of images for the purp oses of face in terms of the probabili ty recognition and database search. Sp eci call y, we argue in favor of a probabilistic measure of S (I ;I )=P ( 2 )=P ( j) (1) 1 2 I I similarity, in contrast to simpler metho ds which where P ( j) is the aposteriori probabilitygiven by I are based on standard L norms (e.g., template 2 Bayes rule, using estimates of the likeliho o d s P (j )and I matching) or subspace-restricted norms (e.g., P (j )which are derived from training data using an E eigenspace matching). The prop osed similarity ecient subspace metho d for density estimation of high- measure is based on a Bayesian analysis of image dimensional data [6]. This Bayesian (MAP) approach di erences: we mo del twomutually exclusive can also b e viewed as a generalized nonlinear extension of classes of variation b etween two facial images: Linear Discriminant Analysis (LDA) [8, 3] or \FisherFace" intra-personal (variations in app earance of the techniques [1] for face recognition.