Face detection and tracking using edge orientation information

An important topic in face recognition as well as in video coding or multi-modal human machine interfaces is the automatic detection of faces or head-and-shoulder regions in visual scenes. The algorithms therefore should be computationally fast enough to allow an online detection and parallel processing of the detected objects. In this paper we describe our ongoing work on face detection using an approach that models the face appearance by edge orientation information. We will show that edge orientation is a powerful local image feature to describe objects like faces or body parts. We will present a sample and efficient method for template matching and object modeling based solely on edge orientation information. We also show how to obtain an optimal face model in the edge orientation domain from a set of training images. Unlike many approaches that model the gray level appearance of the face our approach is computationally very fast. It takes less than 0.1 seconds on a Pentium II 500 MHz for a 190 X 140 image to be processed using a multi- resolution search with three resolution levels. We demonstrate the capability of our detection method on an image database of 7000 images taken from more than 200 different people. The variations in head size, lighting and background are considerable. The obtained detection rate is more than 97% on that database. We have also extended the algorithm for face tracking. The tracking capabilities are shown using results from a real-time implementation of the proposed algorithm.