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Abstract:
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 biological models
of visual recognition.
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