Evaluation of implicit 3D modeling for pose-invariant face recognition

In this paper, we describe and evaluate an approach that uses implicit models of facial features to cope with the problem of recognizing faces under varying pose. The underlying recognition process attaches a parameterized model to every enrolled image that allows the parameter controlled transformation of the stored biometric template into miscellaneous poses within a wide range. We also propose a method for accurate automatic landmark localization in conjunction with pose estimation, which is required by the latter approach. The approach is extensible to other problems in the domain of face recognition for instance facial expression. In the experimental section we present an analysis with respect to accuracy and compare the computational effort with the one of a standard approach.

[1]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

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

[3]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[4]  Thomas Vetter,et al.  Face Recognition Based on Fitting a 3D Morphable Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[6]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[7]  Christoph von der Malsburg,et al.  Pose-invariant face recognition with parametric linear subspaces , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[8]  Simon Baker,et al.  Active Appearance Models Revisited , 2004, International Journal of Computer Vision.

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

[10]  Christoph von der Malsburg,et al.  Analysis and synthesis of human faces with pose variations by a parametric piecewise linear subspace method , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[11]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[12]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

[13]  Shaogang Gong,et al.  An investigation into face pose distributions , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[14]  Norbert Krüger,et al.  Object Recognition with Banana Wavelets , 1997, ESANN.

[15]  Takahiro Ishikawa,et al.  Passive driver gaze tracking with active appearance models , 2004 .

[16]  Ralph Gross,et al.  Appearance-based face recognition and light-fields , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  P J. Phillips,et al.  Face Recognition Vendor Test 2000: Evaluation Report , 2001 .

[18]  P. Jonathon Phillips,et al.  Face recognition vendor test 2002 , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

[19]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[20]  Christoph von der Malsburg,et al.  Analysis, synthesis and recognition of human faces with pose variations , 2001 .

[21]  Jörg Kopecz,et al.  ZN-Face: A system for access control using automated face recognition , 1995, SNN Symposium on Neural Networks.

[22]  Penio S. Penev,et al.  Local feature analysis: A general statistical theory for object representation , 1996 .