Abstract
In this paper, we propose a fusion classification method based on reconstruction error and normalized distance for palmprint recognition. This method first obtains an approximate representation of the test sample by solving a linear system in which the test sample is assumed to be a linear combination of all the original training samples. Then it replaces the test sample by its approximate representation and decomposes the approximate representation as a weighted sum of all the training samples. The proposed method calculates the reconstruction error of the approximate representation from the weighted sum of the training samples from each class. The method also computes the normalized distance between the test sample and each class. Finally, the method integrates the reconstruction error and normalized distance between the test sample and a class to form the matching score and assigns the test sample into the class that has the smallest matching score. Experimental results on the palmprint databases demonstrate the effectiveness of our method.
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References
Janev M, Pekar D, Jakovljevic N, Delic V (2010) Eigenvalues driven Gaussian selection in continuous speech recognition using HMMs with full covariance matrices. Appl Intell 33(2):107–116
Jain AK, Feng J (2009) Latent palmprint matching. IEEE Trans Pattern Anal Mach Intell 31(6):1032–1047
Mahmoud SA, Al-Khatib WG (2011) Recognition of Arabic (Indian) bank check digits using log-Gabor filters. Appl Intell 35(3):445–456
Xu Y, Zhang D, Yang J-Y (2010) A feature extraction method for use with bimodal biometrics. Pattern Recognit 43(3):1106–1115
Zia Uddin M, Lee JJ, Kim T-S (2010) Independent shape component-based human activity recognition via hidden Markov model. Appl Intell 33(2):193–206
Zhang D, Kanhangad V, Luo N, Kumar A (2010) Robust palmprint verification using 2D and 3D features. Pattern Recognit 43:358–368
Valova I, Milano G, Bowen K, Gueorguieva N (2011) Bridging the fuzzy, neural and evolutionary paradigms for automatic target recognition. Appl Intell 35(2):211–225
Zhang D, Song F, Xu Y, Lang Z (2009) Advanced pattern recognition technologies with applications to biometrics. Medical Information Science Reference
Guo Z, Zhang L, Zhang D (2010) Feature band selection for multispectral palmprint recognition. In: ICPR, pp 1136–1139
Dai Q, Bi N, Huang D, Zhang D, Li F (2004) M-band wavelets application to palmprint recognition based on texture features. In: ICIP, pp 893–896
Gui J, Jia W, Zhu L, Wang S-L, Huang D-S (2010) Locality preserving discriminant projections for face and palmprint recognition. Neurocomputing 73(13):2696–2707
Feng G, Hu D, Li M, Zhou Z (2005) Palmprint recognition based on unsupervised subspace analysis. In: ICNC, vol 1, pp 675–678
Lu G, Zhang D, Wang K (2003) Palmprint recognition using eigenpalms features. Pattern Recognit Lett 24(9):1463–1467
Ribaric S, Fratric I (2005) A biometric identification system based on eigenpalm and eigenfinger features. IEEE Trans Pattern Anal Mach Intell 27(11):1698–1709
Cheung KH, Kong W-KA et al (2006) Does eigenpalm work? A system and evaluation perspective. In: ICPR, vol 4, pp 445–448
Sang H, Yuan W, Zhang Z (2009) Research of palmprint recognition based on 2DPCA. In: ISNN, vol 2, pp 831–838
Ekinci M, Aykut M (2008) Palmprint recognition by applying wavelet-based kernel PCA. J Comput Sci Technol 5(23):851–861
Wang Y, Ruan Q (2006) Kernel fisher discriminant analysis for palmprint recognition. In: Proceedings of the 18th international conference on pattern recognition, pp 457–460
Wang M, Ruan Q (2006) Palmprint recognition based on two-dimensional methods. In: Proceedings of the 8th international conference on signal processing
Tao J, Jiang W, Gao Z, Chen S, Wang C (2006) Palmprint recognition based on 2-dimension PCA. In: ICICIC, vol 1, pp 326–330
Pan X, Ruan Q (2008) Palmprint recognition with improved two-dimensional locality preserving projections. Image Vis Comput 26(9):1261–1268
Wu X, Zhang D, Wang K (2003) Fisherpalms based palmprint recognition. Pattern Recognit Lett 24(15):2829–2838
Xu Y, Zhong A, Yang J, Zhang D (2011) Bimodal biometrics based on a representation and recognition approach. Opt Eng 50(3):037202
Jing X-Y, Zhang D (2004) A face and palmprint recognition approach based on discriminant DCT feature extraction. IEEE Trans Syst Man Cybern, Part B, Cybern 34(6):2405–2415
Du F, Yu P, Li H, Zhu L (2011) Palmprint recognition using Gabor feature-based bidirectional 2DLDA. Commun Comput Inf Sci 5(159):230–235
Xu Y, Zhu Q, Zhang D, Yang J-Y (2011) Combine crossing matching scores with conventional matching scores for bimodal biometrics and face and palmprint recognition experiments. Neurocomputing 74:3946–3952
Zhang L et al (2011) Sparse representation or collaborative representation: which helps face recognition? In: ICCV, pp 1–8
Shi Q, Eriksson A, Hengel A, Shen C (2011) Is face recognition really a compressive sensing problem? In: CVPR, pp 553–560
Xu Y, Zhang D, Yang J, Yang J-Y (2011) A two-phase test sample sparse representation method for use with face recognition. IEEE Trans Circuits Syst Video Technol 9(21):1255–1262
Xu Y, Zuo W, Fan Z (2012) Supervised sparse presentation method with a heuristic strategy and face recognition experiments. Neurocomputing 79:125–131
Xu Y, Zhu Q A simple and fast representation-based face recognition method. Neural Comput Appl. doi:10.1007/s00521-012-0833-5
Wright J, Yang AY, Ganesh A et al (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227
Lai Z, Wan M, Jin Z, Yang J (2011) Sparse two-dimensional local discriminant projections for feature extraction. Neurocomputing 74(4):629–637
Xu Y, Zhu Q, Chen Y, Pan J-S (2012) An improvement to the nearest neighbor classifier and face recognition experiments. Int J Innov Comput Inf Control 8(12):1349–4198
Zhang D, Guo Z, Lu G, Zhang L, Zuo W (2010) An online system of multispectral palmprint verification. IEEE Trans Instrum Meas 59(2):480–490
Rowe RK, Uludag UM et al (2007) A multispectral whole-hand biometric authentication system. In: Proc biometric symp, biometric consortium conf, Baltimore, MD, pp 1–6
Wang J-G, Yau W-Y, Suwandy A, Sung E (2008) Person recognition by fusing palmprint and palm vein images based on ‘Laplacianpalm’ representation. Pattern Recognit 41(5):1514–1527
Kong AW-K, Zhang DD, Kamel MS (2009) A survey of palmprint recognition. Pattern Recognit 42(7):1408–1418
Wu X, Wang K, Zhang D (2002) Line feature extraction and matching in palmprint. In: Proceeding of the second international conference on image and graphics, pp 583–590
Kong AW-K, Zhang D (2004) Competitive coding scheme for palmprint verification. In: ICPR, vol 1, pp 520–523
Zuo W, Lin Z, Guo Z, Zhang D (2010) The multiscale competitive code via sparse representation for palmprint verification. In: CVPR, pp 2265–2272
Zhang D, Zuo W, Yue F (2012) A comparative study of palmprint recognition algorithms. ACM Comput Surv 44(1):2
Chu R, Liao S, Han Y, Sun Z, Li SZ, Tan T (2007) Fusion of face and palmprint for personal identification based on ordinal features. In: CVPR, pp 17–22
Huang D, Jia W, Zhang D (2008) Palmprint verification based on principal lines. Pattern Recognit 41:1316–1328
Jing X, Wong H (2006) Biometrics recognition based on fused gaborface and gaborpalm with dcv-rbf feature classification. Electron Lett 21(42):1205–1206
Ekinci M, Aykut M (2007) Gabor-based kernel pca for palmprint recognition. Electron Lett 20(43):1077–1079
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Liu, Z., Pu, J., Huang, T. et al. A novel classification method for palmprint recognition based on reconstruction error and normalized distance. Appl Intell 39, 307–314 (2013). https://doi.org/10.1007/s10489-012-0414-4
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DOI: https://doi.org/10.1007/s10489-012-0414-4