Face detection and tracking using edge orientation information An important topic in face recognition as well as in video coding or multimodal human machine interfaces is the automatic detection of faces or headandshoulder 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 simple 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 grey level appearance of the face our approach is computationally very fast. It takes less than 0.1 seconds on a Pentium II 500MHz for a 190x140 image to be processed using a multiresolution 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.

Tracking faces of arbitrary views for video annotation We proposed an omniface tracking system for video annotation in this paper, which is designed to find faces from arbitrary views in complex scenes. The face detector first locates potential faces in the input by performing skintone detection. The subsequent processing consists of two largely independent components, the frontal face module and the sideview face module, responsible for finding frontalview and sideview faces, respectively. The frontal face module uses a regionbased approach wherein regions of skintone pixels are analyzed for gross oval shape and the presence of facial features. In contrast, the sideview face module follows an edgebased approach to look for curves similar to a sideview profile. To extract the trajectories of faces, the temporal continuity between consecutive frames within the video shots is considered to speed up the tracking process. The main contribution of this work is being able to find faces irrespective of their poses, whereas contemporary systems deal with frontalview faces only. Information regarding to human faces is encoded in XML format for semantic video content representation. The effectiveness of human face for video annotation is demonstrated in a TV program classification system that categorizes the input video clip into predefined types. It is shown that the classification accuracy is improved saliently by the employment of face information.