Face detection using coarse-to-fine support vector classifiers

We describe a new face detection algorithm based on a hierarchy of support vector classifiers (SVM) designed for efficient computation. The hierarchy serves as a platform for a coarse-to-fine search for faces: most of the image is quickly rejected as "background" and the processing naturally concentrates on regions containing faces and face-like structures. The hierarchy is tree-structured: In proceeding from the root to the leaves, the SVM gradually increase in complexity (measured by the number of support vectors) and discrimination (measured by the false alarm rate), but decrease in the level of invariance. Reduced complexity is achieved by clustering support vectors and shifting the decision boundary in order to satisfy a "conservation hypothesis" that preserves positive responses from the original set of support vectors. The computation is organized as a depth-first search and cancel strategy. The gain in efficiency is enormous.