A flexible feature matching for automatic face and facial feature points detection

Abstract

An automatic face and facial feature points (FFPs) detection system is proposed. A face is represented as a graph where the nodes are placed at facial feature points (FFPs) labeled by their Gabor features and the edges describe their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between models and the input image. This matching model works like a random diffusion process in the image space by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background, pose variations and distorted by facial accessories. We demonstrate the benefits of our approach by its implementation on a face identification system.