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Abstract

In face detection, support vector machines (SVM) and neural networks (NN) have been shown to outperform most other classication methods. While both approaches are learning-based,
there are distinct advantages and drawbacks to each method: NNs are difcult to design and
train but can lead to very small and efcient classiers. In comparison, SVM model selection
and training is rather straightforward, and, more importantly, guaranteed to converge to
a globally optimal (in the sense of training errors) solution. Unfortunately, SVM classiers
tend to have large representations which are inappropriate for time-critical image processing
applications.
In this work, we examine various existing and new methods for simplifying support vector
decision rules. Our goal is to obtain efcient classiers (as with NNs) while keeping the numerical
and statistical advantages of SVMs. For a given SVM solution, we compute a cascade
of approximations with increasing complexities. Each classier is tuned so that the detection
rate is near 100. At run-time, the rst (simplest) detector is evaluated on the whole image.
Then, any subsequent classier is applied only to those positions that have been classied as
positive throughout all previous stages. The false positive rate at the end equals that of the
last (i.e. most complex) detector. In contrast, since many image positions are discarded by
lower-complexity classiers, the average computation time per patch decreases signicantly
compared to the time needed for evaluating the highest-complexity classier alone.