Learning of face components in coherent and disturbed constellations

A face recognition system for simultaneous detection and pose estimation is presented. The algorithm proceeds in two steps: At first, separate face components such as eyes, nose and mouth are detected. This is done by a classification of modified SIFT features that are more robust to spatial displacements. Secondly, face-like part constellations are detected by an SVM based voting scheme. Inhibitive votings are introduced to suppress false detections in textured image regions. Experiments on the Feret and Graz data bases demonstrate the high accuracy of the system.