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3D face recognition based on radial basis function network  

Yang, Uk-Il (Yonsei University, Dept. of Electrical & Electronic Engineering, Biometric Engineering Research Center)
Sohn, Kwang-Hoon (Yonsei University, Dept. of Electrical & Electronic Engineering, Biometric Engineering Research Center)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.44, no.2, 2007 , pp. 82-92 More about this Journal
Abstract
This paper describes a novel global shape (GS) feature based on radial basis function network (RBFN) and the extraction method of the proposed feature for 3D face recognition. RBFN is the weighted sum of RBfs, it well present the non-linearity of a facial shape using the linear combination of RBFs. It is the proposed facial feature that the weights of RBFN learned by the horizontal profiles of a face. RBFN based feature expresses the locality of the facial shape even if it is GS feature, and it reduces the feature complexity like existing global methods. And it also get the smoothing effect of the facial shape. Through the experiments, we get 94.7% using the proposed feature and hidden markov model (HMM) to match the features for 100 gallery set with those for 300 test set.
Keywords
3차원 얼굴인식;전역적 형상 특징 추출;방사 기저 함수 신경망;
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1 R. Chellappa, C. L. Wilson, and S. Sirohey, 'Human and machine recognition of faces: A survey,' Proceedings of the IEEE, vol. 83, no. 5, pp. 705-740, May 1995   DOI   ScienceOn
2 W. Zhao, R. Chellappa, A. Rosenfeld, and P.J. Phllips, 'Face recognition: A survey,' CVL Technical Report, Center for Automation Research, University of Maryland at College Park, Oct. 2000
3 G. Gordon, 'Face recognition based on depth and curvature features,' in Computer Vision and Pattern Recognition, pp. 108-110, 1992
4 P. J. Besl and N. D. McKay. 'A method for registration of 3-d shapes,' IEEE Trans. Pat. Anal. and Mach. Intel. vol. 14, No, 2, pp 239-256, Feb 1992   DOI   ScienceOn
5 G. Medioni and R. Waupotitsch, 'Face recognition and modeling in 3D,' in IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pp. 232-233, 2003
6 C. Hesher, A. Srivastava, and G. Erlebacher, 'A novel technique for face recognition using range images,' in Seventh Int'l Symposium on Signal Processing and Its Applications, 2003
7 Intel$circedR$ Open Source Computer Vision Library, http://www.intel.com/software/products/ipp/index. htm
8 J.O. Rawlings, 'Applied Regression Analysis,' Wadsworth & Brooks/Cole, Pacific Grove, CA, 1988
9 J. Y. Cartoux, J. T. LaPreste, and M. Richetin, 'Face authentication or recognition by proifle extraction from range images,' in Proc. of the Workshop on Interpretation of 3D Scenes, pp. 194-199, 1989
10 C. Chua, R. Jarvis, 'Point Signatures: A New Representation for 3D Object Recognition,' International Journal of Computer Vision vol. 25, No. 1, pp. 63-85, 1997   DOI
11 B. Achermann, X. Jiang, and H. Bunke, 'Face recognition using range images,' in International Conference on Virtual Systems and MultiMedia, pp. 129-136, 1997
12 T. Nagamine, T. Uemura, and I. Masuda, '3D facial image analysis for human identification,' in International Conference on Pattern Recognition, pp.324-327, 1992
13 J. C. Lee and E. Milios. 'Matching range images of human faces,' in International Conference on Computer Vision, pp. 722-726, 1990
14 Stan Z. Li, Anil K. Jain, 'Handbook of face recognition', Springer Science+Bussience Media, Inc. 2004
15 H. Song, S. Lee, J. Kim and K.Sohn, '3D sensor based face recognition,' Applied Optics, Vol. 44, No. 5, pp. 677-687, Feb. 2005   DOI