Segmentation, grouping and feature detection for face image analysis

Abstract

Automatic face and facial feature detection by a computer is a computationally challenging problem. There are many shape patterns for eyes, nose and mouth, hair, beards/mustaches, and the overall geometry of the face frame. The texture and color of hair and skin also vary greatly. One source of complexity in analyzing face images is due to the different appearances resulting from different orientations. From the literature and the authors' own experience, it is apparent that the various face orientations, eyeglasses, and hair, are among the main factors affecting the complexity of the face image analysis problems. This paper discusses a system that analyzes face images under a wide range of orientations, like those in the FERET database. Unlike previous systems that were designed specifically for either frontal or side-view images but not both, this takes a unified approach and treats frontal and side-view images with the same common sequence of analysis, with view-specific analysis only at the final stage, amounting to less than 10% of the whole task. It also detects the presence of eyeglasses under all views.