Volume 16, Issue 1 (5-2019)                   JSDP 2019, 16(1): 158-172 | Back to browse issues page

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Imani M, Ghassemian H. Supervised Feature Extraction of Face Images for Improvement of Recognition Accuracy. JSDP. 2019; 16 (1) :158-172
URL: http://jsdp.rcisp.ac.ir/article-1-804-en.html
Tarbiat Modares University
Abstract:   (412 Views)
Dimensionality reduction methods transform or select a low dimensional feature space to efficiently represent the original high dimensional feature space of data. Feature reduction techniques are an important step in many pattern recognition problems in different fields especially in analyzing of high dimensional data. Hyperspectral images are acquired by remote sensors and human face images are one of the high dimensional data types. Because of limitation in the number of training samples, feature reduction is the important preprocessing step for classification of these types of data. Face recognition is one of the main interesting studies in human computer interaction applications. Face is among the most significant biometric characteristics which are used for identification of individuals. Before face recognition, feature reduction is an important processing step. In this paper, we apply the new feature extraction methods, which have been firstly proposed for feature reduction of hyperspectral imagery remote sensing, on the face databases for the first time. In this research, we compare the performance of seven new feature extraction methods with four state-of-the-art feature extraction methods. The proposed methods are Nonparametric Supervised Feature Extraction (NSFE), Clustering Based Feature Extraction (CBFE), Feature Extraction Using Attraction Points (FEUAP), Cluster Space Linear Discriminant Analysis (CSLDA), Feature Space Discriminant Analysis (FSDA), Feature Extraction using Weighted Training samples (FEWT), and Discriminant Analysis- Principal Component 1 (DA-PC1). The experimental results on two face databases, Yale and ORL, show the better performance of some new feature extraction methods, from the recognition accuracy point of view compared to methods such as linear discriminant analysis (LDA), non-parametric weighted feature extraction (NWFE), median-mean line discriminant analysis (MMLDA), and supervised locality preserving projection (LPP).
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Type of Study: Research | Subject: Paper
Received: 2018/02/3 | Accepted: 2019/01/9 | Published: 2019/06/10 | ePublished: 2019/06/10

1. [1] P. Huang, C. Chen, Z. Tang, and Z. Yang, "Discriminant similarity and variance preserving projec-tion for feature extraction", Neurocomput-ing, vol. 139, pp. 180-188, 2014. [DOI:10.1016/j.neucom.2014.02.047]
2. [2] S. Tan, X. Sun, W. Chan, L. Qu , and L. Shao, "Robust Face Recognition With Kernelized Locality-Sensitive Group Sparsity Representa-tion", IEEE Transactions on Image Processing, vol. 26, no. 10, pp. 4661-4668, Oct. 2017. [DOI:10.1109/TIP.2017.2716180] [PMID]
3. [3] Y. Shen, M. Yang, B. Wei, C. T. Chou and W. Hu, "Learn to Recognise: Exploring Priors of Sparse Face Recognition on Smartphones", IEEE Trans-actions on Mobile Computing, vol. 16, no. 6, pp. 1705-1717, June 2017. [DOI:10.1109/TMC.2016.2593919]
4. [4] C. Guzel Turhan and H. S. Bilge, "Class-wise two-dimensional PCA method for face recognition", IET Computer Vision, vol. 11, no. 4, pp. 286-300, 2017. [DOI:10.1049/iet-cvi.2016.0135]
5. [5] W. Wang, R. Wang, Z. Huang, S. Shan and X. Chen, "Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets", IEEE Transactions on Image Processing, vol. 27, no. 1, pp. 151-163, Jan. 2018.
6. [6] Yang, W.-H., Dai, D.-Q., "Two-Dimensional Maximum Margin Feature Extraction for Face Recognition", IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 39, no. 4, pp. 1002-1012, 2009. [DOI:10.1109/TSMCB.2008.2010715] [PMID]
7. [7] S. Ahmadkhani, P. Adibi, "Supervised Probabilistic Principal Component Analysis Mixture Model in a Lossless Dimensionality Reduction Framework for Face Recognition", Quarterly Journal of Signal and Data Processing, vol. 12, no. 4, pp. 53-65, 2016.
8. [8] Yang, M., Zhang, L., Shiu, S. C.-K., and Zhang, D., "Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face Recogni-tion", IEEE Transactions on Information Forensics and Security, vol. 7, no. 6, pp. 1738-1751, 2012. [DOI:10.1109/TIFS.2012.2217332]
9. [9] G. F. Hughes,"On the mean accuracy of statistical pattern recognition," IEEE Transactions on Information Theory, vol. IT-14, no. 1, pp. 55-63, 1968. [DOI:10.1109/TIT.1968.1054102]
10. [10] K. Fukunaga, Introduction to Statistical Pattern Recognition. San Diego, CA, USA: Academic, 1990. [DOI:10.1016/B978-0-08-047865-4.50007-7] [PMID]
11. [11] B.C. Kuo, D.A. Landgrebe, "Nonparametric weighted feature extraction for classification", IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 5, pp. 1096-1105, 2004. [DOI:10.1109/TGRS.2004.825578]
12. [12] J. Xu, J. Yang, Z. Gu, and N. Zhang, "Median-mean line based discriminant analysis", Neuro-computing, vol. 123, pp. 233-246, 2014. [DOI:10.1016/j.neucom.2013.07.012]
13. [13] X.F.He, P.Niyogi, "Locality preserving project-tions", In: Advances in Neural Information Pro-cessing System, vol. 16, pp. 153-160, 2004. [DOI:10.1016/j.ins.2003.08.012]
14. [14] M. Imani, H. Ghassemian, "Nonparametric Supervis Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples", Electronics Industries Quarterly, vol. 4, no.3, Autumn 2013.
15. [15] M. Imani, H. Ghassemian, "Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples", IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 8, pp. 1325-1329, 2014. [DOI:10.1109/LGRS.2013.2292892]
16. [16] M. Imani, H. Ghassemian, "Feature Extraction Using Attraction Points for Classification of Hyperspectral Images in a Small Sample Size Situation", IEEE Geoscience and Remote Sen-sing Letters, vol. 11, no. 11, pp. 1986-1990, 2014. [DOI:10.1109/LGRS.2014.2316134]
17. [17] M. Imani, H. Ghassemian, "Classification of Hyperspectral Images Using Cluster Space Linear Discriminant Analysis and Small Training Set", Iranian Journal of Electrical and Computer Engineering, vol. 14, no. 1, pp. 73-81, June 2016.
18. [18] M. Imani, H. Ghassemian, "Feature space discriminant analysis for hyperspectral data feature reduction", ISPRS Journal of Photo-grammetry and Remote Sensing, vol. 102, pp. 1-13, 2015. [DOI:10.1016/j.isprsjprs.2014.12.024]
19. [19] M. Imani, H. Ghassemian, "Feature Extraction Using Weighted Training Samples", IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 7, pp. 1387 - 1386, 2015. [DOI:10.1109/LGRS.2015.2402167]
20. [20] M. Imani, H. Ghassemian, "Feature reduction of hyperspectral images: discriminant analysis and the first principal component", Journal of AI and Data Mining, vol. 3, no. 1, pp.1-9, 2015. [DOI:10.5829/idosi.JAIDM.2015.03.01.01]
21. [21] G. H. Golub, and C. F.van Loan, Matrix Computations, 3rd ed. Baltimore, MD, USA: The Johns Hopkins Univ. Press, 1996.
22. [22] M. Yang, N. Ahuja, and D. Kriegman, "Face recognition using kernel eigenfaces", Proc. International Conference on Image processing, 2000, pp. 37-40.
23. [23] V. D. M Nhat, and S. Lee, "Kernel-based 2DPCA for Face Recognition", Proc. IEEE International Symposium on Signal Processing and Infor-mation Technology, IEEE, December. 2007, pp. 35-39. [DOI:10.1109/ISSPIT.2007.4458104]

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