Facial Parts Detection by Using Four Directional Features and Relaxation Matching

Abstract

Positions of facial parts, such as eyes, mouth, and nose, are required for constructing the user interface by face image recognition or for face pose estimation. We propose a facial parts detection method that is robust for face postures. The method uses four directional features and relaxation matching. Facial part templates based on four directional features learned from many face images correspond to varieties of input pattern. The initial probability is obtained by template matching of the four directional features. Positions of facial parts are detected by relaxation matching using the spring connection of each facial part. Since it corresponds to various face postures, the multi-direction integrated model is used. The proposal method is short computation time and useful for real-time system. Experiments show that facial parts are detectable to the HOIP multi-directional face image database. Detection rate was 96% for ±45°horizontal and ±30°vertical face postures.