Active Appearance Models for Face Recognition

A growing number of applications are starting to use face recognition as the initial step towards interpreting human actions, intention, and behaviour, as a central part of next-generation smart environments. Recognition of facial expressions is an important example of face-recognition techniques used in these smart environments. In order to be able to recognize faces, there are some difficulties to overcome. Faces are highly variable, deformable objects, and can have very different appearances in images depending on pose, lighting, expression, and the identity of the person. Besides that, face images can have different backgrounds, differences in image resolution, contrast, brightness, sharpness, and colour balance. This paper describes a model-based approach, called Active Appearance Models, for the interpretation of face images, capable of overcoming these difficulties. This method is capable of ‘explaining’ the appearance of a face in terms of a compact set of model parameters. Once derived, this model gives the opportunity for various applications to use it for further investigations of the modelled face (like characterise the pose, expression, or identity of a face). The second part of this paper describes some variations on Active Appearance Models aimed at increasing the performance and the computational speed of Active Appearance Models.