Face Recognition Based on 2D and 3D Features

This paper presents a completly automated face recognition system integrating both two dimensional (texture) and three dimensional (shape) features. We introduce a novel fusion strategy that allows to automatically select, for each face, the most relevant features from each modality. The performance is evaluated on the largest public data corpus for face recognition currently available, the Face Recognition Grand Challenge version 2.0.

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

[2]  Josef Kittler,et al.  Audio- and Video-Based Biometric Person Authentication, 5th International Conference, AVBPA 2005, Hilton Rye Town, NY, USA, July 20-22, 2005, Proceedings , 2005, AVBPA.

[3]  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.

[4]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[5]  Tieniu Tan,et al.  Automatic 3D face recognition combining global geometric features with local shape variation information , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

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

[7]  Patrick J. Flynn,et al.  A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition , 2006, Comput. Vis. Image Underst..

[8]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[9]  P. Jonathon Phillips,et al.  Face Recognition Vendor Test 2002 Performance Metrics , 2003, AVBPA.

[10]  Ya-Ping Wong,et al.  Dense stereo correspondence based on recursive adaptive size multi-windowing , 2003 .

[11]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Feng Han,et al.  3D human face recognition using point signature , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[13]  Shaoyan Zhang,et al.  Face recognition with support vector machine , 2003, IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003.

[14]  Gérard G. Medioni,et al.  Performance of Geometrix ActiveID^TM 3D Face Recognition Engine on the FRGC Data , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[15]  Neill W Campbell,et al.  IEEE International Conference on Computer Vision and Pattern Recognition , 2008 .

[16]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[17]  Paola Campadelli,et al.  A face recognition system based on automatically determined facial fiducial points , 2006, Pattern Recognit..

[18]  Mohammed Bennamoun,et al.  An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[20]  Tieniu Tan,et al.  Face Verification Based on Bagging RBF Networks , 2006, ICB.

[21]  Majid Ahmadi,et al.  N-feature neural network human face recognition , 2004, Image Vis. Comput..