Face recognition from depth and curvature
Face recognition from depth and curvature
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust real-time 3D head pose estimation from range data
Pattern Recognition
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We propose a way to locate facial features from a video sequence captured by a camcorder undergoing strong translational motion. Pairs of stereo images containing frontal views of the human subject are sampled from the video sequence. A multiresolution hierarchical matching algorithm finds point correspondences over a large disparity range. The task of locating facial features such as the eyes, nose and mouth is aided by depth information derived from the matching data. We present experimental results to verify our approach.