Face and Facial Feature Localization

In this paper we present a general technique for face and facial feature localization in 2D color images with arbitrary background. In a previous work we studied an eye localization module, while here we focus on mouth localization. Given in input an image that depicts a sole person, first we exploit the color information to limit the search area to candidate mouth regions, then we determine the exact mouth position by means of a SVM trained for the purpose. This component-based approach achieves the localization of both the faces and the corresponding facial features, being robust to partial occlusions, pose, scale and illumination variations. We report the results of the separate modules of the single feature classifiers and their combination on images of several public databases

Face analysis using curve edge maps

This paper proposes an automatic and real-time system for face analysis, usable in visual communication applications. In this approach, faces are represented with Curve Edge Maps, which are collections of polynomial segments with a convex region. The segments are extracted from edge pixels using an adaptive incremental linear-time fitting algorithm, which is based on constructive polynomial fitting. The face analysis system considers face tracking, face recognition and facial feature detection, using Curve Edge Maps driven by histograms of intensities and histograms of relative positions. When applied to different face databases and video sequences, the average face recognition rate is 95.51%, the average facial feature detection rate is 91.92% and the accuracy in location of the facial features is 2.18% in terms of the size of the face, which is comparable with or better than the results in literature. However, our method has the advantages of simplicity, real-time performance and extensibility to the different aspects of face analysis, such as recognition of facial expressions and talking