Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Human Face Recognition Using Point Signature
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Recognition Using Range Images
VSMM '97 Proceedings of the 1997 International Conference on Virtual Systems and MultiMedia
Face Modeling and Recognition in 3-D
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Three-Dimensional Model Based Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
An Evaluation of Multimodal 2D+3D Face Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Computer Vision and Image Understanding
Expression-invariant 3D face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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In this paper, we propose an efficient 3D face recognition method based on statistics of range image differences. Each pixel value of range image represents normalized depth value of corresponding point on facial surface, and so depth differences between two range images' pixels of the same position on face can straightforwardly describe the differences between two faces' structures. Here, we propose to use histogram proportion of depth differences to discriminate intra and inter personal differences for 3D face recognition. Depth differences are computed from a neighbor district instead of direct subtraction to avoid the impact of non-precise registration. Furthermore, three schemes are proposed to combine the local rigid region(nose) and holistic face to overcome expression variation for robust recognition. Promising experimental results are achieved on the 3D dataset of FRGC2.0, which is the most challenging 3D database so far.