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Journal of Sensors
Volume 2016 (2016), Article ID 6859364, 8 pages
Research Article

3D Face Recognition Using Anthropometric and Curvelet Features Fusion

Dan Song,1,2 Jing Luo,1,2,3 Chunyuan Zi,1,2 and Huixin Tian1,2

1College of Electrical Engineering and Automation, Tianjin Polytechnic University, Tianjin 300387, China
2Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin 300387, China
3School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Sydney, NSW 2522, Australia

Received 30 June 2015; Revised 22 September 2015; Accepted 7 October 2015

Academic Editor: Pietro Siciliano

Copyright © 2016 Dan Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Curvelet transform can describe the signal by multiple scales, and multiple directions. In order to improve the performance of 3D face recognition algorithm, we proposed an Anthropometric and Curvelet features fusion-based algorithm for 3D face recognition (Anthropometric Curvelet Fusion Face Recognition, ACFFR). First, the eyes, nose, and mouth feature regions are extracted by the Anthropometric characteristics and curvature features of the human face. Second, Curvelet energy features of the facial feature regions at different scales and different directions are extracted by Curvelet transform. At last, Euclidean distance is used as the similarity between template and objectives. To verify the performance, the proposed algorithm is compared with Anthroface3D and Curveletface3D on the Texas 3D FR database. The experimental results have shown that the proposed algorithm performs well, with equal error rate of 1.75% and accuracy of 97.0%. The algorithm we proposed in this paper has better robustness to expression and light changes than Anthroface3D and Curveletface3D.