Robust facial landmark detection for intelligent vehicle system

This paper presents an integrated approach for robustly locating facial landmark for drivers. In the first step a cascade of probability learners is used to detect the face edge primitives from fine to coarse, so that faces with variant head poses can be located. The edge density descriptors and skin-tone color features are combined together as the basic features to examine the probability of an edge being a face primitive. A cascade of the probability learner is used. In each scale, only edges with sufficient large probabilities are kept and passed on to the next scale. The final output of the cascade gives the edge primitives that belong to faces, which determine the face. location. In the second step, a facial landmark detection procedure is applied on the segmented face pixels. Facial landmark candidates are first detected by learning the posteriors in multiple resolutions. Then geometric constraint and the local appearance, modeled by SIFT descriptor, are used to find the set of facial landmarks with largest matching score. Experiments over high-resolution images (FERET database) as well as the real-world drivers' data are used to evaluate the performance. A fairly good results can be obtained, which validates the proposed approach.