Face Recognition From Video using Active Appearance Model Segmentation

Face recognition from video can be improved if good face segmentation of the subject under test is achieved. Many video based face recognition rely on simple background modeling and coarse alignment strategies for segmentation. This work presents a face recognition from video framework based on using Active Appearance Models (AAM) to achieve accurate face segmentation and consistent shape free representation across a video sequence. The segmentation provided by the AAM can be effectively normalized (morphed) to a mean shape. The resulting subimage can then be delivered to conventional face recognition from video algorithms for robust classification. We present preliminary results on a dataset of 17 individuals and outline the problems encountered in this approach.