JACIII Vol.22 No.1 pp. 141-146
doi: 10.20965/jaciii.2018.p0141


3D Face Recognition Based on Regional Shape Maps

Xiaoni Wang

School of Applied Science, Beijing Information Science and Technology University
No. 12, Qing He Xiao Ying East Road, Haidian District, Beijing, P. R. China

April 21, 2017
November 14, 2017
January 20, 2018
3D face recognition, regional shape map, iterative closest shape point, corresponding point

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.

Cite this article as:
X. Wang, “3D Face Recognition Based on Regional Shape Maps,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.1, pp. 141-146, 2018.
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Last updated on Jul. 12, 2024