This study proposes an iterative closest shape point (ICSP) registration method based on regional shape maps for 3D face recognition. A neutral expression image randomly selected from a face database is considered as the reference face. The point-to-point correspondences between the input face and the reference face are achieved by constructing the points’ regional shape maps. The distance between corresponding point pairs is then minimized by iterating through the correspondence findings and coordinate transformations. The vectors composed of the closest shape points obtained in the last iteration are regarded as the feature vectors of the input face. These 3D face feature vectors are finally used for both training and recognition using the Fisherface method. Experiments are conducted using the 3D face database maintained by the Chinese Academy of Science Institute of Automation (CASIA). The results show that the proposed method can effectively improve 3D face recognition performance.

1. [1] Y. Ming, “Research on 3D Face Recognition based on Invariant Features,” Ph.D. Thesis (Beijing Jiaotong University), 2013.
2. [2] C. M. Ye, “Studies on Key Issues of 3D Face Recognition,” PhD. Thesis (Hefei University of Technology).
3. [3] M. Ardabilian, P. Szeptycki, D. Huang et al., “3D Face Recognition,” Signal and Image Processing for Biometrics, pp. 89-115, 2012.
4. [4] P. B. Sharma and M. M. Goyani, “3D Face Recognition Techniques-A Review,” Int. J. of Engineering Research and Applications, pp. 787-793, 2012.
5. [5] P. J. Besl and H. D. McKay, “A method for registration of 3-D shapes,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.14, Issue 2, pp. 239-256, 1992.
6. [6] B. B. Amor, M. Ardabilian, and L. Chen, “New Experiments on ICP-Based 3D Face Recognition and Authentication,” The 18th Int. Conf, on Pattern Recognition: Volume 4, pp. 1521-1526, 2006.
7. [7] T. Russ, C. Boehnen, and T. Peters, “3D Face Recognition Using 3D Alignment for PCA,” IEEE Trans. on Pattern Analysis and Machine Intelligence, pp. 1063-1074, 2003.
8. [8] K. K. Zhao, J. T. Xi et al., “3D Face recognition based on a modified ICP method,” Int. Conf. on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, pp. 101-109, 2011.
9. [9] H. Mohammadzade and D. Hatzinakos, “Iterative Closest Normal Point for 3D Face Recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.35, Issue 2, pp. 381-397, 2013.
10. [10] CASIA-3D FaceV1, http://biometrics.idealtest.org/ [accessed December 29, 2017]