Face Recognition Robust to Head Pose from One Sample Image

Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, Adaptive Principal Component Analysis (APCA) [4], which performs well against both lighting variation and expression change. But like other eigenface-derived face recognition algorithms, APCA only performs well with frontal face images. The work presented in this paper is an extension of our previous work to also accommodate variations in head pose. Following the approach of Cootes et al, we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. We use a Viola-Jones based face detector to detect the face in real-time and thus solve the initialization problem for our Active Appearance Model search. E...

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Brian C. Lovell, Shaokang Chen, Ting Shan
• Keywords: Computer Vision, Face Recognition Algorithm, Face Recognition Algorithms, ICPR 2006, Most Face Recognition