Matching 2.5D Scans for Face Recognition

Abstract. The performance of face recognition systems that use twodimensional images is dependent on consistent conditions such as lighting, pose, and facial appearance. We are developing a face recognition system that uses three-dimensional depth information to make the system more robust to these arbitrary conditions. We have developed a face matching system that automatically correlates points in three dimensions between two 2.5D range images of different views. A hybrid Iterative Closest Point (ICP) scheme is proposed to integrate two classical ICP algorithms for fine registration of the two scans. A robust similarity metric is defined for matching purpose. Results are provided on a preliminary database of 10 subjects (one training image per subject) containing frontal face images of neutral expression with a testing database of 63 scans that varied in pose, expression and lighting.

• Type: Conference
• Year: 2004
• Where: ICBA
• Authors: Xiaoguang Lu, Dirk Colbry, Anil K. Jain
• Keywords: Biometrics, Face Recognition, Face Recognition Systems, Frontal Face Images, ICBA 2004