Abstract
This study proposes a framework to integrate the Gaborface features and the matrix based feature extraction method for face recognition. In this framework, we first select a subset of Gaborfaces to construct the optimal ensemble Gaborface. Then, a two-phase matrix based feature extraction method, i.e.: two-dimensional linear discriminant analysis (2DLDA) plus multi-subspaces principle component analysis (MSPCA), is developed to directly and effectively extract features from the optimal ensemble Gaborface matrixes. Experiment results on ORL and AR face datasets demonstrate the effectiveness of our method.
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References
Xu, Y., Zhong, A., Yang, J., Zhang, D.: LPP solution schemes for use with face recognition. Pattern Recognition 43(12), 4165–41761 (2010)
Fan, Z., Xu, Y., Zhang, D.: Local linear discriminant analysis framework using sample neighbors. IEEE Transactions on Neural Networks 22(7), 1119–11321 (2011)
Chen, Y.-W., Xu, R., Ushikome, A.: Serially-connected dual 2d pca for efficient face representation and face recognition. International Journal of Innovative Computing, Information and Control 5(11), 4367–4372
Xu, Y., Zhang, D., Yang, J., Jin, Z., Yang, J.: Evaluate dissimilarity of samples in feature space for improving KPCA. International Journal of Information Technology & Decision Making 10(3), 479–495 (2011)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognitive Neurosci. 3(1), 71–86 (1991)
Xu, Y., Zhang, D., Jin, Z., Li, M., Yang, J.-Y.: A fast kernel-based nonlinear discriminant analysis for multi-class problems. Pattern Recognition 39(6), 1026–1033 (2006)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Machine Intell. 19(7), 711–720 (1997)
Gao, Q., Zhang, L., Zhang, D.: Face Recognition using FLDA with Single Training Image Per-person. Applied Mathematics and Computation 205, 726–734 (2008)
Xu, Y., Zhang, D., Yang, J., Yang, J.Y.: An approach for directly extracting features from matrix data and its application in face recognition. Neurocomputing 71, 1857–1865 (2008)
Gao, Q., Zhang, L., Zhang, D., Xu, H.: Independent components extraction from image matrix. Pattern Recognition Letters 31(3), 171–178 (2010)
Yang, W., Wang, J., Ren, M., Zhang, L., Yang, J.: Feature extraction using fuzzy inverse FDA. Neurocomputing 72(13-15), 3384–3390 (2009)
Zhang, Q., Zhou, C.J., Zhao, J.: Face Recognition Based on FLDA, CPCA and Improved HMM. International Journal of Innovative Computing, Information and Control 6(2), 801–808 (2010)
Li, M., Yuan, B.Z.: 2D-LDA: A statistical linear discriminant analysis for image matrix. Pattern Recognition Letters 26, 527–532 (2005)
Xu, Y., Zhang, D.: Represent and fuse bimodal biometric images at the feature level: complex-matrixbased fusion scheme. Optical Engineering 49(3) (2010)
Wang, L., et al.: 2D Gaborface representation method for face recognition with ensemble and multichannel model. Image and Vision Computing 26, 820–828 (2008)
Lee, Y.-C., Chen, C.-H.: A Gabor Feature Based Horizontal and Vertical Discriminant for Face Verification. International Journal of Innovative Computing, Information and Control 9(5), 2111–2123 (2013)
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© 2014 Springer International Publishing Switzerland
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Zhu, Q., Xu, Y., Lu, Y., Wen, J., Fan, Z., Li, Z. (2014). Novel Matrix Based Feature Extraction Method for Face Recognition Using Gaborface Features. In: Pan, JS., Krömer, P., Snášel, V. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 238. Springer, Cham. https://doi.org/10.1007/978-3-319-01796-9_38
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DOI: https://doi.org/10.1007/978-3-319-01796-9_38
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01795-2
Online ISBN: 978-3-319-01796-9
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