3D Face Recognition Founded on the Structural Diversity of Human Faces

We present a systematic procedure for selecting facial fiducial points associated with diverse structural characteristics of a human face. We identify such characteristics from the existing literature on anthropometric facial proportions. We also present three dimensional (3D) face recognition algorithms, which employ Euclidean/geodesic distances between these anthropometric fiducial points as features along with linear discriminant analysis classifiers. Furthermore, we show that in our algorithms, when anthropometric distances are replaced by distances between arbitrary regularly spaced facial points, their performances decrease substantially. This demonstrates that incorporating domain specific knowledge about the structural diversity of human faces significantly improves the performance of 3D human face recognition algorithms.

[1]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[2]  A. Ben Hamza,et al.  Geodesic matching of triangulated surfaces , 2006, IEEE Transactions on Image Processing.

[3]  P. Phillips,et al.  1 FACE RECOGNITION VENDOR TEST 2002 : EVALUATION REPORT , 2003 .

[4]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Juan Comas,et al.  Manual of physical anthropology , 1960 .

[6]  Chin-Seng Chua,et al.  Facial feature detection and face recognition from 2D and 3D images , 2002, Pattern Recognit. Lett..

[7]  Jake K. Aggarwal,et al.  Three dimensional face recognition based on geodesic and Euclidean distances , 2007, Electronic Imaging.

[8]  A. Bovik,et al.  Advances and Challenges in 3 D and 2 D + 3 D Human Face Recognition , 2007 .

[9]  José F. Vélez,et al.  Face recognition using 3D surface extracted descriptors , 2003 .

[10]  Anil K. Jain,et al.  Matching 2.5D face scans to 3D models , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Kwanghoon Sohn,et al.  Local Feature Based 3D Face Recognition , 2005, AVBPA.

[12]  Gaile G. Gordon,et al.  Face recognition based on depth and curvature features , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Christoph von der Malsburg,et al.  Strategies and Benefits of Fusion of 2D and 3D Face Recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[14]  Alexander M. Bronstein,et al.  Three-Dimensional Face Recognition , 2005, International Journal of Computer Vision.

[15]  B O Rogers,et al.  The role of physical anthropology in plastic surgery today. , 1974, Clinics in plastic surgery.

[16]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[17]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, CAIP.

[18]  Penio S. Penev,et al.  Local feature analysis: A general statistical theory for object representation , 1996 .

[19]  L. Farkas,et al.  Anthropometric Facial Proportions in Medicine , 1986 .

[20]  Alexander M. Bronstein,et al.  Expression-invariant three-dimensional face recognition , 2005 .

[21]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[22]  L. Whitaker Anthropometry of the Head and Face in Medicine. , 1983 .

[23]  Matthew Stone,et al.  An anthropometric face model using variational techniques , 1998, SIGGRAPH.

[24]  C. Roberts Practical Anthropometry , 1888, British medical journal.

[25]  J. Vélez,et al.  Face recognition using 3D local geometrical features: PCA vs. SVM , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..