2D and 3D face recognition: A survey
Pattern Recognition Letters
3D Face Recognition Benchmarks on the Bosphorus Database with Focus on Facial Expressions
Biometrics and Identity Management
3D Face Recognition Using Joint Differential Invariants
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Nasal Region-Based 3D Face Recognition under Pose and Expression Variations
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Extracting Structured Topological Features from 3D Facial Surface: Approach and Applications
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Regional registration for expression resistant 3-D face recognition
IEEE Transactions on Information Forensics and Security
Anthropometric 3D Face Recognition
International Journal of Computer Vision
Selection and extraction of patch descriptors for 3D face recognition
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Automatic 3d face feature points extraction with spin images
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Rank-Based decision fusion for 3d shape-based face recognition
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
3D facial feature localization for registration
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
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In this paper, we address the use of three dimensional facial shape information for human face identification. We propose a new method to represent faces as 3D registered point clouds. Fine registration of facial surfaces is done by first automatically finding important facial landmarks and then, establishing a dense correspondence between points on the facial surface with the help of a 3D face template-aidedthin plate spline algorithm. After the registration of facial surfaces, similarity between two faces is defined as a discrete approximation of the volume difference between facial surfaces. Experiments done on the 3D RMA dataset show that the proposed algorithm performs as good as the point signature method, and it is statistically superior to the point distribution model-based method and the 2D depth imagery technique. In terms of computational complexity, the proposed algorithm is faster than the point signature method.