Face Recognition: Technical Challenges and Research Directions
Face recognition performance has improved significantly since the first automatic face recognition system developed by Kanade Face detection, facial feature extraction, and recognition can now be performed in “realtime” for images captured under favorable, constrained situations Although progress in face recognition has been encouraging, the task has also turned out to be a difficult endeavor, especially for unconstrained tasks where viewpoint, illumination, expression, occlusion, accessories, and so on vary considerably In this talk, I will analyze challenges from the viewpoint of face manifolds and points out possible research directions towards highly accurate face recognition I will show that the challenges come from high nonconvexity of face manifolds, in the image space, under variations in lighting, pose and so on; unfortunately, there have been no good methods from theories of pattern recognition for solving such difficult problems, especially when the size of training data is small However, there are two directions to look at towards possible solutions: One is to construct a “good” feature space in which the face manifolds become less complex i.e., less nonlinear and nonconvex than those in other spaces This includes two levels of processing: (1) normalize face images geometrically and photometrically, such as using morphing and histogram equalization; and (2) extract features in the normalized images which are stable with respect to the said variations, such as based on Gabor wavelets The second strategy is to construct classification engines able to solve less, although still, nonlinear problems in the feature space, and to generalize better A successful algorithm usually combines both strategies Still another direction is on system design, including sensor hardware, to make the pattern recognition problems thereafter less challenging.