 Introduction  Identity Data Set and Face Representation  Associate-Predict model  Switching Mechanism  Experimental Results

 Appearance-based for face recognition  Inevitable obstacle  Associate-Predict model  The studies of brain theories

 Identity data set  Face representation

 200 identities from the Multi-PIE data set  7 pose  4 illumination

 Representation at the facial component level  12 facial components  Face F = (f1, f2,..., f12) › fi for each component

 Appearance-prediction model  Likelihood-prediction model

 Two input faces  Setting : S A, S B › A and B are facial components  Select the specific face image setting is equal to S B › component A’ from this image

 d A = |f A ' − f B | › distance between the components  d B = |f B ' − f A |  Final distance between A and B: 1/2 (d A + d B )

 Adaptive distance d p  αA and αB : weight  After the “appearance-prediction” on all 12 facial components, we can obtain a new composite face

 Using classifier measure the likelihood of B belonging to A  Positive training samples › Input face › the K most alike generic identities

 Implement this switching mechanism › facial components : A and B › settings : S A = { P A, L A } and S B = { P B, L B }  Categorize the input pair into two classes › “comparable” › “not comparable” › based on the difference of S A and S B

 Comparable class › {|PA − PB| < 3 } and {|LA − LB| < 3 }  Not comparable class › the rest situations

 The final matching distance d sw › d a : the direct appearance matching › d p : the associate-predict model

 Experiments on the Multi-PIE and LFW data sets  Basic comparisons  Results on benchmarks

 Holistic vs. Component

 Positive sample size › number of positive samples is *k › “1” is the input sample › K is the selected number of top- alike associated identities

 K = 3 as the default parameter

 Switching mechanism › the switch model can effectively improve the results on both benchmark

 Multi-PIE benchmark  LFW benchmark