Bernd Heisele, Thomas Serre, Massimiliano Pontil, Thomas Vetter, Tomaso Poggio
We describe an algorithm for automatically learning discriminative com- ponents of objects with SVM classiﬁers. It is based on growing image parts by minimizing theoretical bounds on the error probability of an SVM. Component-based face classiﬁers are then combined in a second stage to yield a hierarchical SVM classiﬁer. Experimental results in face classiﬁcation show considerable robustness against rotations in depth and suggest performance at signiﬁcantly better level than other face detection systems. Novel aspects of our approach are: a) an algorithm to learn component-based classiﬁcation experts and their combination, b) the use of 3-D morphable models for training, and c) a maximum operation on the output of each component classiﬁer which may be relevant for bio- logical models of visual recognition.