Using multi-instance enrollment to improve performance of 3D face recognition
Computer Vision and Image Understanding
Automatic 3D face recognition from depth and intensity Gabor features
Pattern Recognition
Nasal Region-Based 3D Face Recognition under Pose and Expression Variations
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Technical Section: Expression modeling for expression-invariant face recognition
Computers and Graphics
Regional registration for expression resistant 3-D face recognition
IEEE Transactions on Information Forensics and Security
Design and development of automatic visual inspection system for PCB manufacturing
Robotics and Computer-Integrated Manufacturing
Robust sparse bounding sphere for 3D face recognition
Image and Vision Computing
Real-time 3D face recognition using line projection and mesh sampling
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
Computer Vision and Image Understanding
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The addition of Three Dimensional (3D) data has the potential to greatly improve the accuracy of Face Recognition Technologies by providing complementary information. In this paper a new method combining intensity and range images and providing insensitivity to expression variation based on Log-Gabor Templates is presented. By breaking a single image into 75 semi-independent observations the reliance of the algorithm upon any particular part of the face is relaxed allowing robustness in the presence of occulusions, distortions and facial expressions. Also presented is a new distance measure based on the Mahalanobis Cosine metric which has desirable discriminatory characteristics in both the 2D and 3D domains. Using the 3D database collected by University of Notre Dame for the Face Recognition Grand Challenge (FRGC), benchmarking results are presented demonstrating the performance of the proposed methods.