Feature fusion of palmprint and face via tensor analysis and curvelet transform

• Autorzy: Xu X. , Guan X. , Zhang D. , Zhang X. , Deng W.
• Języki publikacji: EN

Abstrakty

EN

In order to improve the recognition accuracy of the unimodal biometric system and to address the problem of the small samples recognition, a multimodal biometric recognition approach based on feature fusion level and curve tensor is proposed in this paper. The curve tensor approach is an extension of the tensor analysis method based on curvelet coefficients space. We use two kinds of biometrics: palmprint recognition and face recognition. All image features are extracted by using the curve tensor algorithm and then the normalized features are combined at the feature fusion level by using several fusion strategies. The k-nearest neighbour (KNN) classifier is used to determine the final biometric classification. The experimental results demonstrate that the proposed approach outperforms the unimodal solution and the proposed nearly Gaussian fusion (NGF) strategy has a better performance than other fusion rules.

Słowa kluczowe

EN

tensor analysis; curvelet; feature fusion; multimodal biometric recognition

• Wydawca: Stowarzyszenie Elektryków Polskich ; Polska Akademia Nauk ; Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
• Czasopismo: Opto-Electronics Review
• Rocznik: 2012
• Tom: Vol. 20, No. 2
• Strony: 138--147
• Opis fizyczny: Bibliogr. 34 poz., il., rys., wykr.

Twórcy

autor

Xu X.

autor

Guan X.

autor

Zhang D.

autor

Zhang X.

autor

Deng W.

• School of Electronics and Information Engineering, Xi'an Jiaotong University, 28 Xian'ning West Road, Xi'an, 710049 People's Republic of China,

Bibliografia

• [1] K. Jain: Biometric recognition. Nature 449, 38-40 (2007).
• [2] R. Jenkins and A. M. Burton: 100% accuracy in automatic face recognition. Science 319, 435 (2008).
• [3] C. C. Tsai, J. Taur, and C. W. Tao: Iris recognition based on relative variation analysis with feature selection. Opt. Eng. 47, 097202 (2008).
• [4] F. P. Nie, S. M. Xiang, Y. Q. Song, and C. S. Zhang: Orthogonal locality minimizing globality maximizing projections for feature extraction. Opt. Eng. 48, 017202-017202-5, (2009).
• [5] K. Chang, K. Bowyer, and V. Barnabas: Comparison and combination of ear and face images in appearance-based biometrics. IEEE T. Pattern Anal. 25, 1160-1165 (2003).
• [6] L. Hong and A. K. Jain: Integrating faces and fingerprints for personal identification. IEEE T. Pattern Anal. 20, 1295-1307 (1998).
• [7] A. K. Jain and A. Ross: Learning user-specific parameters in a multibiometric system. Proc. ICIP, Vol. 1, 57-60 (2002).
• [8] A. Ross and A. K. Jain: Information fusion in biometrics: Pattern Recogn. Lett. 24, 2115–2125 (2003).
• [9] X. Y. Jing, Y. F. Yao, and D. Zhang: Face and palmprint pixel level fusion and Kernel DCV-RBF classifier for small sample biometric recognition. Pattern Recogn. 40, 3209-3224 (2007).
• [10] A. Ross and R. Govindarajan: Feature level fusion using hand and face biometrics”, Proc. SPIE. 5779, 196-204 (2005).
• [11] T. H. Zhang, X. L. Li, D. C. Tao, and J. Yang: Multimodal biometrics using geometry preserving projections. Pattern Recogn. 41, 805-812 (2008).
• [12] X. L. Zhou and B. Bhanu: Feature fusion of side face and gait for video−based human identification. Pattern Recogn. 41, 778-795 (2008).
• [13] Y. F. Yao, X. Y. Jing, and H. S. Wong: Face and palmprint feature level fusion for single sample biometrics recognition. Neurocomputing 70, 1582-1588 (2007).
• [14] W. Zhang, Z. C. Lin, and X. O. Tang: Tensor linear Laplacian discrimination (TLLD) for feature extraction. Pattern Recogn. 42, 1941-1948 (2009).
• [15] M. A. O. Vasilescu and D. Terzopoulos: Multilinear subspace analysis for image ensembles. Proc. CVPR, 93-99 (2003).
• [16] X. He, D. Cai, and P. Niyogi: Tensor subspace analysis. Proc. NIPS (2005).
• [17] D. C. Tao, X. L. Li, and X. D. Wu: General tensor discriminant analysis and Gabor features for gait recognition. IEEE T. Pattern Anal. 29, 1700-1715 (2007).
• [18] H. P. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos: MPCA: multilinear principal component analysis of tensor objects. IEEE T. Neural. Networ. 19, 18-39 (2008).
• [19] S. T. Li and B. Yang: Multifocus image fusion by combining curvelet and wavelet transform. Pattern Recogn. Lett. 29, 1295-1301 (2008).
• [20] E. J. Candes, L. Demanet, and D. L. Donoho: Fast discrete curvelet transforms. Multiscale Mode. Sim. 5, 861-899 (2005).
• [21] T. Mandal: Curvelet based face recognition via dimension reduction. Signal Process. 89, 2345-2353 (2009).
• [22] X. Xu, D. Zhang, and X. Zhang: An efficient method for human face recognition using nonsubsampled contourlet transform and support vector machine. Opt. Appl. 39, 601-615 (2009).
• [23] M. Y. Fu and C. Zhao: Fusion of infrared and visible images based on the Second generation curvelet transform. J. Infrared Milim. W. 28, 254-258(2009).
• [24] Z. H Xie, G. D Liu, and S. Q Wu: A fast infrared face recognition system using curvelet transformation. Proc. 2th Inter. Symp. Electro. Comm. Secu. 2, 145-149 (2009).
• [25] D. Zhang, W. Kong, and J. You: Online palmprint identification. IEEE. T. Pattern. Anal. 25, 1041-1050 (2003).
• [26] Y. Freund: Boosting a weak learning algorithm by majority. Inform. Comput. 121, 256-285 (1995).
• [27] W. Hao-jing, W. Jian-li, and W. Ming-hao: Efficient image inpainting based on bilinear interpolation downscaling. Optic. Precis. Eng. 18, 1234-1241 (2010).
• [28] S. A. Fahmy: Generalised parallel bilinear interpolation architecture for vision systems. Proc. Int. Conf. on Reconfigurable Computing and FPGAs, Cancun, 331-336 (2008).
• [29] X. He, S. Yan, and Y. Hu: Face recognition using Laplacian faces. IEEE T. Pattern Anal. 27, 328-340 (2005).
• [30] C. Du, J. Yang, and Q. Wu: Integrating affinity propagation clustering method with linear discriminant analysis for face recognition. Opt. Eng. 46, 110501 (2007).
• [31] D. Xu, S. Yan, and D. Tao: Marginal fisher analysis and its variants for human gait recognition and content-based image retrieval. IEEE T. Image Process. 216, 2811-2821 (2007).
• [32] J. Ye, R. Janardan, and Q. Li: Two-dimensional linear discriminant analysis. http://books.nips.cc/nips17. Html, 10-20 (2007).
• [33] PolyU Palmprint Database.TTTP://www.comp.polyu.edu. hk/~biometrics/
• [34] W. Jia, D. S. Huang, and D. Zhang: Palmprint verification based on robust line orientation code. Pattern Recogn. 41, 1504-1513 (2008).