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Unformatted text preview: 1 Lecture13 Face Recognition & Visual Lipreading Face Recognition 2 Simple Approach Recognize faces (mug shots) using gray levels (appearance) Each image is mapped to a long vector of gray levels Several views of each person are collected in the modelbase during training During recognition a vector corresponding to an unknown face is compared with all vectors in the modelbase The face from modelbase, which is closest to the unknown face is declared as a recognized face. Problems and Solution Problems : Dimensionality of each face vector will be very large (250,000 for a 512X512 image!) Raw gray levels are sensitive to noise, and lighting conditions. Solution: Reduce dimensionality of face space by finding principal components (eigen vectors) to span the face space Only a few most significant eigen vectors can be used to represent a face, thus reducing the dimensionality 3 Eigen Vectors and Eigen Values The eigen vector, x, of a matrix A is a special vector, with the following property x Ax l = Where ? is called eigen value ) det( = I A l To find eigen values of a matrix A first find the roots of: Then solve the following linear system for each eigen value to find corresponding eigen vector ) ( = x I A l Example  = 7 4 3 2 1 A 1 , 3 , 7 3 2 1 = = = l l l = = = 1 , 2 1 , 4 4 1 3 2 1 x x x Eigen Values Eigen Vectors 4 Eigen Values ) det( = I A l ) 1 1 1 7 4 3 2 1 det( =   l ) 7 4 3 2 1 det( =  l l l 7 3, , 1 ) 7 )( 3 )( 1 ( ) ) 7 )( 3 )(( 1 ( = = = = = l l l l l l l l l Eigen Vectors ) ( = x I A l =...
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This note was uploaded on 06/12/2011 for the course CAP 6411 taught by Professor Shah during the Spring '09 term at University of Central Florida.
 Spring '09
 Shah

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