One of the fundamental problems in computer vision is to make a machine automatically recognize an object in a query image based on previously seen examples. This problem becomes particularly difficult when the machine is trying to recognize an object among several with rather similar appearances, and the object is partially occluded or disguised. This is often the case with human face recognition. This project aims to explore new mathematical tools from sparse representation in signal processing, by casting robust face recognition as a (sparse) error correction problem. Recently, sparse error correction based on minimizing the 1-norm has seen great success in signal processing. This project will investigate its potential in image-based object recognition despite occlusion or corruption, especially for human faces. Preliminary experimental results have shown good promise of this new approach. In its one-year span, this project aims to study the special geometric and statistical models and problems associated with human face recognition and hopes to develop even more robust and scalable face recognition algorithms. To verify the results, a prototype face recognition system will be developed, with an emphasis on theoretical and algorithmic progress. All the results will be available at a public website: http://perception.csl.uiuc.edu/recognition/Home.html
