An efficient method for human face recognition using nonsubsampled contourlet transform and support vector machine

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

Abstrakty

EN

To improve the recognition rate in different conditions, a multiscale face recognition method based on nonsubsampled contourlet transform and support vector machine is proposed in this paper. Firstly, all face images are decomposed by using nonsubsampled contourlet transform. The contourlet coefficients of low frequency and high frequency in different scales and various angles will be obtained. Most significant information of faces is contained in coefficients, which is important for face recognition. Then, the combinations of coefficients are applied as study samples to the support vector machine classifiers. Finally, the decomposed coefficients of testing face image are used to test classifiers, then face recognition results are obtained. The experiments are performed on the YaleB database and the Cambridge University ORL database. The results indicate that the method proposed has performs better than the wavelet-based method. Compared with the wavelet-based method, the proposed method can make the best recognition rates increase by 2.85% for YaleB database and 1.87% for ORL database, respectively. Our method is also suitable for other face databases and appears to work well.

Słowa kluczowe

EN

multiscale geometric analysis; face recognition; nonsubsampled; contourlet; support vector machine (SVM)

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2009
• Tom: Vol. 39, nr 3
• Strony: 601--615
• Opis fizyczny: bibliogr. 33 poz.,

Twórcy

autor

Xu X.

autor

Zhang D.

autor

Zhang X.

• School of Electronics and Information Engineering, Xi'an Jiaotong University, 28 Xianning West Road, Xi'an 710049, P.R. China

Bibliografia

• [1] TAIPING ZHANG, BIN FANG, YUAN YUAN, YUAN YAN TANG, ZHAOWEI SHANG, DONGHUI LI, FANGNIAN LANG, Multiscale facial structure representation for face recognition under varying illumination, Pattern Recognition 42(2), 2009, pp. 251–258.
• [2] WING-PONG CHOI, SIU-HONG TSE, KWOK-WAI WONG, KIN-MAN LAM, Simplified Gabor-wavelets for human face recognition, Pattern Recognition 41(6), 2008, pp. 1186–1199.
• [3] HUIYU ZHOU, YUAN YUAN, ABDUL H. SADKA, Application of semantic features in face recognition, Pattern Recognition 41(10), 2008, pp. 3251–3256.
• [4] JADHAV D.V., HOLAMBE R.S., Feature extraction using Radon and wavelet transforms with application to face recognition, Neurocomputing 72(7–9), 2009, pp. 1951–1959.
• [5] DELAC K., GRGIC M., GRGIC S., Face recognition in JPEG and JPEG2000 compressed domain, Image and Vision Computing (in press).
• [6] TAN X., CHEN S., ZHOU Z.H., ZHANG F., Face recognition from a single image per person: A survey, Pattern Recognition 39(9), 2006, pp. 1725–1745.
• [7] KOBEL J., SUCHWALKO A., PODBIELSKA H., Application of thermal imaging for human face recognition, Optica Applicata 32(4), 2002, pp.653–664. 614 XUEBIN XU, DEYUN ZHANG, XINMAN ZHANG
• [8] RONGNIAN TANG, JIUQIANG HAN, XINMAN ZHANG, An effective iris location method with high robustness, Optica Applicata 37(3), 2007, pp. 295–303.
• [9] VUCINI E., GOKMEN M., GROLLER M.E., Face recognition under varying illumination, The 15th International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision, 2007, pp. 57–64.
• [10] KUANG-CHIH LEE, HO J., KRIEGMAN D.J., Acquiring linear subspaces for face recognition under variable lighting, IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 2005, pp. 684–698.
• [11] JIANMING LU, XUE YUAN, TAKASHI YAHAGI, A method of face recognition based on fuzzy c-means clustering and associated sub-NNs, IEEE Transactions on Neural Networks 18(1), 2007, pp. 150–160.
• [12] XUDONG XIE, KIN-MAN LAM, Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image, IEEE Transactions on Image Processing 15(9), 2006, pp. 2481–2492.
• [13] ZHANG B.L., ZHANG H., GE S.S., Face recognition by applying wavelet subband representation and kernel associative memory, IEEE Transactions on Neural Networks 15(1), 2004, pp. 166–177.
• [14] CHEN G.Y., XIE W.F., Pattern recognition with SVM and dual-tree complex wavelets, Image and Vision Computing 25(6), 2007, pp. 960–966.
• [15] STARCK J.L., CANDES E.J., DONOHO D.L., The curvelets transform for image denoising, IEEE Transactions on Image Processing 11(6), 2002, pp. 670–684.
• [16] TANAYA MANDAL, ANGSHUL MAJUMDAR, Q.M. JONATHAN WU, Face recognition by curvelet based feature extraction, International Conference on Intelligent Automation and Robotics, LNCS 4633,2007, pp. 806–817.
• [17] DO M.N., VETTERLI M., The contourlet transform: an efficient directional multiresolution image representation, IEEE Transactions on Image Processing 14(12), 2005, pp. 2091–2106.
• [18] YANG L., GUO B.L., NI W., Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform, Neurocomputing 72(1–3), 2008, pp. 203–211.
• [19] LU Y., DO M.N., A new contourlet transform with sharp frequency localization, IEEE International Conference on Image Processing, 2006, pp. 1629–1632.
• [20] HANLONG YU, SHENGSHENG YU et al., An image compression scheme based on modified contourlet transform, Computer Engineering and Application 41(1), 2005, pp. 40–43.
• [21] JUN YAN, MURALEEDHARAN R., XIANG YE, OSADCIW L.A., Contourlet based image compression for wireless communication in face recognition system, IEEE International Conference on Communication, 2008, pp. 505–509.
• [22] HEDIEH SAJEDI, MANSOUR JAMZAD, A contourlet-based face detection method in color images, Proceedings – International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007, pp. 727–732.
• [23] BIN YANG, SHUTAO LI, FENGMEI SUN, Image fusion using nonsubsampled contourlet transform, Proceedings of the 4th International Conference on Image and Graphics, ICIG 2007, pp. 719–724.
• [24] ZHOU J., CUNHA A.L., M.N. DO., Nonsubsampled contourlet transform: construction and application in enhancement, Proceedings – International Conference on Image Processing, ICIP 2005, Vol. 1, pp. 469–472.
• [25] DA CUNHA A.L., JIANPING ZHOU, DO M.N., The nonsubsampled contourlet transform: theory, design, and applications, IEEE Transactions on Image Processing 15(10), 2006, pp. 3089–3101.
• [26] ESLAMI R., RADHA H., Wavelet-based contourlet transform and its application to image coding, Proceedings – International Conference on Image Processing, ICIP 2004, Vol. 2, pp. 3189–3192.
• [27] QIANG ZHANG, BAO-LONG GUO, Multifocus image fusion using the nonsubsampled contourlet transform, Signal Processing 89(7), 2009, pp. 1334–1346.
• [28] CHAHIRA SERIEF, MOURAD BARKAT, YOUCEF BENTOUTOU, MALEK BENSLAMA, Robust feature points extraction for image registration based on the nonsubsampled contourlet transform, AEU –International Journal of Electronics and Communications 63(2), 2009, pp. 148–152.ss
• [29] XINMAN ZHANG, JIUQIANG HAN, PEIFEI LIU, Restoration and fusion optimization scheme of multifocus image using genetic search strategies, Optica Applicata 35(4), 2005, pp. 927–942.
• [30] VAPNIC V., Statistical Learning Theory, Wiley, New York, 1998.
• [31] CHASANIS V., LIKAS A., GALATSANOS N., Simultaneous detection of abrupt cuts and dissolves in videos using support vector machines, Pattern Recognition Letters 30(1), 2009, pp. 55–65.
• [32] FRANC V., HLAVAC V., Greedy kernel principal component analysis, Cognitive Vision Systems:Sampling the Spectrum of Approaches, Vol. 3948 LNCS, 2006, pp. 87–105.
• [33] GUNTER S., SCHRAUDOLPH N.N., VISHWANATHAN S.V.N., Fast iterative kernel principal component analysis, Journal of Machine Learing Research 8, 2007, pp. 1893–1918.