Joint Boosting Feature Selection for Robust Face Recognition

A fundamental challenge in face recognition lies in determining what facial features are important for the identification of faces. In this paper, a novel face recognition framework is proposed to address this problem. In our framework, 3D face models are used to synthesize a huge database of realistic face images which covers wide appearance variations of faces due to various pose, illumination, and expression changes. A novel feature selection algorithm which we call Joint Boosting is developed to extract discriminative face features using this massive database. The major contributions of this paper are: (1) With the help of 3D face models, a massive database of realistic virtual face images is generated to achieve robust feature selection; (2)Because the huge database covers a wide range of face variations, our feature selection procedure only needs to be trained once, and the selected feature set can be generalized to other face database without re-training; (3) We propose a new l...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Rong Xiao, Wu-Jun Li, Yuandong Tian, Xiaoou Tang
• Keywords: Computer Vision, CVPR 2006, Discriminative Face Features, Face Database, Face Recognition Framework, Realistic Face Images, Realistic Virtual Face