Abstract
A novel subspace learning algorithm based on nearest feature line and directional derivative gradient is proposed in this paper. The proposed algorithm combines neighborhood discriminant nearest feature line analysis and directional derivative gradient to extract the local discriminant features of the samples. A discriminant power criterion based on nearest feature line is used to find the most discriminant direction in this paper. Some experiments are implemented to evaluate the proposed algorithm and the experimental results demonstrate the effectiveness of the proposed algorithm.
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Li, J.-B., Chu, S.-C., Pan, J.-S., Jain, L.C.: Multiple Viewpoints Based Overview for Face Recognition. Journal of Information Hiding and Multimedia Signal Processing 3, 352–369 (2012)
Krinidis, S., Pitas, I.: Statistical Analysis of Human Facial Expressions. Journal of Information Hiding and Multimedia Signal Processing 1, 241–260 (2010)
Zhang, L., Zhang, L., Zhang, D., Guo, Z.: Phase Congruency Induced Local Features for Finger-Knuckle-Print Recognition. Pattern Recognition 45, 2522–2531 (2012)
Wu, J., Zhou, Z.H.: Face Recognition with One Training Image Per Person. Pattern Recognition Letters 23, 1711–1719 (2002)
Belhumenur, P.N., Hepanha, J.P., Kriegman, D.J.: Eigenfaces vs Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Analysis Machine Intelligence 19, 711–720 (1997)
Li, J.-B., Pan, J.-S., Lu, Z.-M.: Kernel Optimization-Based Discriminant Analysis for Face Recognition. Neural Computing and Applications 18, 603–612 (2009)
Yang, J., Frangi, A.F., Yang, J.-Y., Zhang, D., Jin, Z.: KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 230–244 (2005)
Li, J.-B., Yu, L.-J., Sun, S.-H.: Refined Kernel Principal Component Analysis Based Feature Extraction. Chinese Journal of Electronics 20, 467–470 (2011)
Pan, J.-S., Li, J.-B., Lu, Z.-M.: Adaptive Quasiconformal Kernel Discriminant Analysis. Neurocomputing 71, 2754–2760 (2008)
Li, J.-B., Pan, J.-S., Chen, S.-M.: Kernel Self-Optimized Locality Preserving Discriminant Analysis for Feature Extraction and Recognition. Neurocomputing 74, 3019–3027 (2011)
Li, J.-B., Pan, J.-S., Chu, S.-C.: Kernel Class-wise Locality Preserving Projection. Information Sciences 178, 1825–1835 (2008)
Li, J.-B., Gao, H., Pan, J.-S.: Common Vector Analysis of Gabor Features with Kernel Space Isomorphic Mapping for Face Recognition. International Journal of Innovative Computing, Information and Contro 6(10) (October 2010)
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, 1026–1033 (2006)
Xu, Y., Zhang, D.: Represent and Fuse Bimodal Biometric Images at the Feature Fevel: Complex-Matrix-Based Fusion Scheme. Opt. Eng. 49, 037002 (2010)
Yang, J., Zhang, D., Frangi, A.F., Yang, J.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 26, 131–137 (2004)
Yang, J., Zhang, D., Yong, X., Yang, J.Y.: Two-dimensional discriminant transform for face recognition. Pattern Recognition 37, 1125–1129 (2005)
Yan, L., Pan, J.-S., Chu, S.-C., Muhammad, K.K.: Adaptively weighted sub-directional two-dimensional linear discriminant analysis for face recognition. Future Generation Computer Systems 28, 232–235 (2012)
Li, S.Z., Lu, J.: Face Recognition Using the Nearest Feature Line Method. IEEE Trans. Neural Networks 10, 439–443 (1999)
Feng, Q., Pan, J.-S., Yan, L.: Restricted Nearest Feature Line with Ellipse for Face Recognition. Journal of Information Hiding and Multimedia Signal Processing 3, 297–305 (2012)
Feng, Q., Pan, J.-S., Yan, L.: Nearest Feature Centre Classifier for Face Recognition. Ellectronics Letters 48, 1120–1122 (2012)
Zheng, Y.-J., Yang, J.-Y., Yang, J., Wu, X.-J., Jin, Z.: Nearest Neighbour Line Nonparametric Discriminant Analysis for Feature Extraction. Electronics Letters 42, 679–680 (2006)
Yang, Y., Yuan, Y., Li, X.: Generalised Nearest Feature Line for Subspace Learning. Electronics Letters 43, 1079–1080 (2007)
Lu, J., Tan, Y.P.: Uncorrelated Discriminant Nearest Feature Line Analysis for Face Recognition. IEEE Signal Processing Letter 17, 185–188 (2010)
Yan, L., Pan, J.-S., Zheng, W., Chu, S.-C., Roddick, J.F.: Neighborhood Discriminant Nearest Feature Line Analysis for Face Recognition. Journal of Internet Technology 14, 344–347 (2013)
Yan, L., Wang, C., Chu, S.-C., Pan, J.-S.: Discriminant Analysis Based on Nearest Feature Line. In: Yang, J., Fang, F., Sun, C. (eds.) IScIDE 2012. LNCS, vol. 7751, pp. 356–363. Springer, Heidelberg (2013)
Zuniga, O.A., Haralick, R.M.: Integrated Directional Derivative Gradient Operator. IEEE Trans. Systems, Man, and Cybernetics 17, 508–517 (1987)
Martinez, A.M., Benavente, R.: The AR Face Database. CVC Technical Report 24 (1998)
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Yan, L., Pan, JS., Zhu, X. (2014). Directional Discriminant Analysis for Image Feature Extraction. 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_37
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DOI: https://doi.org/10.1007/978-3-319-01796-9_37
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01795-2
Online ISBN: 978-3-319-01796-9
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