Abstract
In this paper, we present an algorithm to detect and track both frontal and side faces in video clips. By means of both learning Haar-like features of human faces and boosting the learning accuracy with InfoBoost algorithm, our algorithm can detect frontal faces in video clips. Furthermore, we map these Haar-like features to a 3D model to create the classifier that can detect both frontal and side faces. Since it is costly to detect and track faces using the 3D model, we project Haar-like features from the 3D model to a 2D space in order to generate various face orientations. By using them, we can detect even side faces in real time by only learning frontal faces.
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