Unconstrained Face Recognition with User Interaction

Face recognition in unconstrained environment is very challenging due to the variations in facial expression, face pose, light condition and occlusion (e.g. eye glasses or hair). A recent project led by Dr. Learned-Miller at University of Massachusetts, Amherst, published the test results on 13,000 images of faces collected from web. The results are described in an ROC curve. At 10% false positive rate, the best true position rate (recognition rate) is around 67%. That is, in 100 face images for one person, only 67 faces are correctly recognized as this person; in 100 faces images of other persons, 10 faces are incorrectly recognized as this person. Therefore there is still big gap between this performance and the requirements of security applications, for example, access control. But are these results useful for other practical applications?  

The answer is YES, but with user interaction. Facebook, Google’s Picasa, and Apple’s iPhoto, have integrated face recognition into their photo management systems. With no exception, auto-tagging of the photos is assisted with user interaction. User interaction can correct the mis-recognized faces, and refine the recognition algorithms towards 100% recognition rate and 0% false positive rate. 

In March 24, Face.com launches face recognition applications for Facebook.com. The technology (Face Finder) used by Face.com is said to be the descriptor based method described in the following paper: 

Lior Wolf, Tal Hassner, and Yaniv Taigman, Descriptor Based Methods in the Wild, Faces in Real-Life Images Workshop in European Conference on Computer Vision (ECCV), 2008. [pdf] [webpage]

The matlab code for Local Binary Pattern (LBP) descriptor is also available online. It has the best performance among the methods tested in UMass’ test. You may want to try it out.

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