Abstract
In this paper, an efficient model of palmprint identification is presented based on subspace density estimation using Gaussian Mixture Model (GMM). While a few training samples are available for each person, we use intrapersonal palmprint deformations to train the global GMM instead of modeling GMMs for every class. To reduce the dimension of such variations while preserving density function of sample space, Principle Component Analysis (PCA) is used to find the principle differences and form the Intrapersonal Deformation Subspace (IDS). After training GMM using Expectation Maximization (EM) algorithm in IDS, a maximum likelihood strategy is carried out to identify a person. Experimental results demonstrate the advantage of our method compared with traditional PCA method and single Gaussian strategy.
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D. Zhang, W. Kong, J. You. Online palmprint identification. IEEE Trans. on PAMI., 25(2003)9, 1041–1050.
G. Lu, D. Zhang, K. Wang. Palmprint recognition using eigenpalms features. Patter Recognition Letters, 24(2003)9, 1463–1467.
C. Han, H. Cheng, C. Lin, K. Fan. Personal authentication using palm-print features. Pattern Recognition, 36(2003)2, 371–381.
J. You, W. Li, D. Zhang. Hierarchical palmprint identification via multiple feature extraction. Pattern Recognition, 35(2002)4, 847–859.
W. Li, D. Zhang, Z. Xu. Palmprint recognition based on Fourier transform. Chinese Journal of Software, 13(2002)5, 879–886.
X. Wu, D. Zhang, K. Wang. Fisherpalms based palmprint recognition. Pattern Recognition Letters, 24(2003)15, 2829–2838.
X. Wu, K. Wang, D. Zhang. HMMs based palmprint identification. In Proceedings of Biometric Authentication: First International Conference, Hong Kong, China, July 15–17, 2004, LNCS 3702, Springer-Verlag, 775–781.
B. Moghaddam, A. Pentland. Probabilistic visual learning for object representation. IEEE Trans. on PAMI., 19(1997)7, 696–710.
C. Sanderson, S. Bengio. Face verification using synthesized non-Frontal models. IDIAP Research Report, IDIAP-RR 03-60, Martigny, Switzerland, 2003.
B. Moghaddam. Principal manifolds and probabilistic subspaces for visual recognition. IEEE Trans. on PAMI., 24(2002)6, 780–788.
T. Wu, C. Lin, R. C. Weng. Probability estimates for multi-class classification by pairwise coupling. Journal of Machine Learning Research, 5(2004)12, 975–1005.
B. Moghaddam, C. Nastar, A. Pentland. A Bayesian similarity measure for deformable image matching. Image and Vision Computing, 19(2001)5, 235–244.
X. Wu, D. Zhang, K. Wang, B. Huang. Palmprint classification using principle lines. Pattern Recognition, 37(2004)10, 1987–1998.
N. Vasconcelos, G. Carneiro. What is the role of independence for visual recognition? Technical Report, Compaq Cambridge Research Laboratory, Cambridge, MA, 2002.
S. J. Raudys, A. K. Jain. Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans. on PAMI., 13(1001)3, 252–264.
A. K. Jain, R. P. W. Duin, J. Mao. Statistical pattern recognition: a review. IEEE Trans. on PAMI., 22(2000)1, 4–37.
R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification. John Wiley & Sons, New York, NY, USA, 2001, 124–128.
P. M. Baggenstoss. Statistical modeling using gaussian mixtures and HMMS with matlab. Technical Report, Naval Undersea Warfare Center, Rhode Island, 2002.
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Li, Q., Qiu, Z. & Sun, D. Modeling intrapersonal deformation subspace using GMM for palmprint identification. J. of Electron.(China) 23, 543–548 (2006). https://doi.org/10.1007/s11767-004-0208-x
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DOI: https://doi.org/10.1007/s11767-004-0208-x