Face recognition software uses sparse image representation techniques

The conventional way to recognize a face is to record distance between the eyes, width of the mouth, shape of the nose. But new research shows that recording random points is faster in crowds and more effective with disguises. That research by UC Berkeley research scientist Allen Yang exploits a new algorithm called sparse representation. This website introduces the mathematical framework for classification and recognition problems in computer vision especially face recognition. The idea is to cast recognition as a sparse representation problem, using mathematical tools from compressed sensing and L1 minimization. This leads to scalable algorithms for face recognition based on linear or convex programming. These algorithms produce striking results, recognizing subjects across large databases despite severe corruption and occlusion. For more information, go to: http://perception.csl.uiuc.edu/recognition/Home.html