3D Face Recognition based on Deformation Invariant Image using Symbolic LDA

Face recognition is one of the most important abilities that the humans possess. There are several reasons for the growing interest in automated face recognition, including rising concerns for public security, the need for identity verification for physical and logical access to shared resources, and the need for face analysis and modeling techniques in multimedia data management and digital entertainment. In recent years, significant progress has been made in this area, with a number of face recognition and modeling systems have been developed and deployed. However, accurate and robust face recognition still offers a number of challenges to computer vision and pattern recognition researchers, especially under unconstrained environments. In this paper, a novel deformation invariant image based 3D face recognition is proposed. The experiments are done using the 3D CASIA Face Database, which includes 123 individuals with complex expressions. Experimental results show that the proposed method substantially improves the recognition performance under various facial expressions.

[1]  Patrick J. Flynn,et al.  An evaluation of multimodal 2D+3D face biometrics , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[3]  Michael G. Strintzis,et al.  Face localization and authentication using color and depth images , 2005, IEEE Transactions on Image Processing.

[4]  Patrick J. Flynn,et al.  Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Li Li,et al.  3D face recognition by constructing deformation invariant image , 2008, Pattern Recognit. Lett..

[6]  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..

[7]  Tieniu Tan,et al.  3D Face Recognition Based on G-H Shape Variation , 2004, SINOBIOMETRICS.

[8]  Francesco Palumbo,et al.  Principal component analysis of interval data: a symbolic data analysis approach , 2000, Comput. Stat..

[9]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Michael G. Strintzis,et al.  3-D Face Recognition With the Geodesic Polar Representation , 2007, IEEE Transactions on Information Forensics and Security.

[11]  Nick Pears,et al.  Isoradius Contours: New Representations and Techniques for 3D Face Registration and Matching , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[12]  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..

[13]  Anil K. Jain,et al.  Deformation Modeling for Robust 3D Face Matching , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Ioannis A. Kakadiaris,et al.  Evaluation of 3D Face Recognition in the presence of facial expressions: an Annotated Deformable Model approach , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[15]  Alexander M. Bronstein,et al.  Expression-Invariant Representations of Faces , 2007, IEEE Transactions on Image Processing.

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

[17]  Hans-Hermann Bock,et al.  Analysis of Symbolic Data , 2000 .