kbbn09iccv - Attribute and Simile Classifiers for Face...

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Unformatted text preview: Attribute and Simile Classifiers for Face Verification Neeraj Kumar Alexander C. Berg Peter N. Belhumeur Shree K. Nayar Columbia University * Abstract We present two novel methods for face verification. Our first method – “attribute” classifiers – uses binary classi- fiers trained to recognize the presence or absence of de- scribable aspects of visual appearance ( e.g ., gender, race, and age). Our second method – “simile” classifiers – re- moves the manual labeling required for attribute classifica- tion and instead learns the similarity of faces, or regions of faces, to specific reference people. Neither method re- quires costly, often brittle, alignment between image pairs; yet, both methods produce compact visual descriptions, and work on real-world images. Furthermore, both the attribute and simile classifiers improve on the current state-of-the-art for the LFW data set, reducing the error rates compared to the current best by 23 . 92% and 26 . 34% , respectively, and 31 . 68% when combined. For further testing across pose, illumination, and expression, we introduce a new data set – termed PubFig – of real-world images of public figures (celebrities and politicians) acquired from the internet. This data set is both larger (60,000 images) and deeper (300 images per individual) than existing data sets of its kind. Finally, we present an evaluation of human performance. 1. Introduction There is enormous variability in the manner in which the same face presents itself to a camera: not only might the pose differ, but so might the expression and hairstyle. Mak- ing matters worse – at least for researchers in computer vi- sion – is that the illumination direction, camera type, focus, resolution, and image compression are all almost certain to differ as well. These manifold differences in images of the same person have confounded methods for automatic face recognition and verification, often limiting the reliability of automatic algorithms to the domain of more controlled set- tings with cooperative subjects [ 33 , 3 , 29 , 16 , 30 , 31 , 14 ]. Recently, there has been significant work on the “La- beled Faces in the Wild” (LFW) data set [ 19 ]. This data set is remarkable in its variability, exhibiting all of the differences mentioned above. Not surprisingly, LFW has proven difficult for automatic face verification methods [ 25 , 34 , 17 , 18 , 19 ]. When one analyzes the failure cases for * { neeraj,aberg,belhumeur,nayar } @cs.columbia.edu Figure 1: Attribute Classifiers: An attribute classifier can be trained to recognize the presence or absence of a describable as- pect of visual appearance. The responses for several such attribute classifiers are shown for a pair of images of Halle Berry. Note that the “flash” and “shiny skin” attributes produce very differ- ent responses, while the responses for the remaining attributes are in strong agreement despite the changes in pose, illumination, ex- pression, and image quality. We use these attributes for face verifi-pression, and image quality....
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This note was uploaded on 11/03/2009 for the course COMPUTERS CS537 taught by Professor Salman during the Spring '09 term at Texas A&M University–Commerce.

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kbbn09iccv - Attribute and Simile Classifiers for Face...

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