Abstract
This chapter discusses the problems faced by present day face recognition systems in the presence of extreme variations. Even though face recognition technology [215] has progressed from linear subspace methods [216] such as eigenfaces and fisher faces [217–219] to nonlinear methods such as KPCA, KFD [220–223], many of the problems are yet to be addressed completely. In addition to challenges such as expression and pose variations, partial occlusions, the face recognition techniques face a major bottle neck in the form of illumination variation. The chapter addresses the problems of expression variations and partial occlusions by presenting a novel feature selection strategy. The illumination variations are tackled by considering images from multiple sensors.
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