Performance study of Face Recognition systems using LBP and ICA descriptors with sparse representation - MRLSR and KNN Classifiers, respectively

Performance study of Face Recognition systems using LBP and ICA descriptors with sparse representation - MRLSR and KNN Classifiers, respectively

  IJCTT-book-cover
 		 International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-42 Number-1
Year of Publication : 2016
Authors : K Sarath, G. Sreenivasulu
DOI :  10.14445/22312803/IJCTT-V42P106

MLA

K Sarath, G. Sreenivasulu  "Performance study of Face Recognition systems using LBP and ICA descriptors with sparse representation - MRLSR and KNN Classifiers, respectively". International Journal of Computer Trends and Technology (IJCTT) V42(1):33-41, December 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
In this paper an attempt has been to study and compare the results of two scenarios, i.e. 1) LBP descriptor with sparse representation (MRSLR) classifier and 2) ICA descriptor with KNN classifier, of a face recognition system, on a standard image data sets for training and testing with/without noise, occultation and different illumination facial images. The results show that the second scenario ICA + KNN exhibits better performance, compared to the first scenario LBP + MRSLR as descriptors and classifiers, respectively. The results show that the ICA + KNN scenario exhibits better performance, with a faster recognition speed and more recognition accuracy.

References
1. P.Dollár, V. Rabaud, G. Cottrell and S. Belongie, “Behavior recognition via sparse spatio-temporal features”, In 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005 (pp. 65-72). IEEE.
2. C. Schuldt, I. Laptev, and B. Caputo, “ Recognizing human actions: a local SVM approach”, In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on (Vol. 3, pp. 32-36). IEEE.
3. P.N. Belhumeur, J. P. Hespanha and D. J. Kriegman, "Eigen faces vs. fisherfaces: Recognition using class specific linear projection." IEEE Transactions on pattern analysis and machine intelligence19, no. 7 (1997): 711-720.
4. R. Brunelli, and T. Poggio,” Face recognition: Features versus templates”, IEEE transactions on pattern analysis and machine intelligence, 1993, 15(10), pp.1042-1052.
5. X. Sun, M. Chen, A. Hauptmann. “Action recognition via local descriptors and holistic features”, In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2009 Jun 20 (pp. 58-65). IEEE.
6. W. Zhao, R. Chellappa, P.J. Phillips,” Subspace linear discriminant analysis for face recognition”. Computer Vision Laboratory, Center for Automation Research, University of Maryland, 1999 Apr.
7. E. Oja, “ Principal components, minor components, and linear neural networks”, Neural networks,1992, 5(6), 927-935.
8. Q. Liu, H. Lu, S. Ma, “Improving kernel Fisher discriminant analysis for face recognition”. IEEE Transactions on Circuits and Systems for Video Technology. 2004 Jan;14(1):42-9.
9. S. Tseng, “ Comparison of holistic and feature based approaches to face recognition” (Doctoral dissertation, School of Computer Science and Information Technology, Faculty of Applied Science, Royal Melbourne Institute of Technology), 2003.
10. P. Campadelli, R. Lanzarotti, C. Savazzi. “A feature-based face recognition system”, In Image Analysis and Processing, 2003. Proceedings. 12th International Conference on 2003 Sep 17 (pp. 68-73). IEEE.
11. Ahonen T, Hadid A, Pietikainen M. “Face description with local binary patterns: Application to face recognition”, IEEE transactions on pattern analysis and machine intelligence, 2006, 28(12), 2037-2041.
12. B.A. Draper, K.Baek, M.S.Bartlett, J.R.Beveridge,” Recognizing faces with PCA and ICA”, Computer vision and image understanding. 2003 Aug 31;91(1):115-37.
13. H.Oja, K. Nordhausen, "Independent component analysis.", Encyclopedia of Environmetrics (2001).
14. L.Wang, H.Wu, C.Pan, “Manifold regularized local sparse representation for face recognition”, IEEE Transactions on Circuits and Systems for Video Technology. 2015 Apr;25(4):651- 9.
15. A.J.Bell, T.J.Sejnowski, “ An information-maximization approach to blind separation and blind deconvolution”, Neural computation, 1995 Nov;7(6):1129-59.
16. G.Heusch, Y.Rodriguez, S.Marcel, “Local binary patterns as an image preprocessing for face authentication”, In 7th International Conference on Automatic Face and Gesture Recognition (FGR06) 2006 Apr 2 (pp. 6-pp). IEEE.
17. T.Huynh, R. Min, J.L. Dugelay, “An efficient LBP-based descriptor for facial depth images applied to gender recognition using RGB-D face data”, In Asian Conference on Computer Vision, 2012,(pp. 133-145). Springer Berlin Heidelberg.
18. H.Tang, B.Yin, Y. Sun, Y.Hu, “3D face recognition using local binary patterns”, Signal Processing, 2013 93(8), 2190-2198.
19. M. Abdulrahman, T.R. Gwadabe, F.J. Abdu, A. Eleyan, “Gabor wavelet transform based facial expression recognition using PCA and LBP”, In 2014 22nd Signal Processing and Communications Applications Conference (SIU), 2014, (pp. 2265-2268). IEEE.
20. R.Shyam, Y.N.Singh, “Face recognition using augmented local binary pattern and Bray Curtis dissimilarity metric”, In Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference, 2015,(pp. 779-784). IEEE.
21. S.Jain, M. Durgesh, T. Ramesh, “Facial Expression Recognition Using Variants of LBP and Classifier Fusion”, In Proceedings of International Conference on ICT for Sustainable Development, 2016, (pp. 725-732). Springer Singapore
22. M.S.Bartlett, J.R.Movellan, T.J.Sejnowski,” Independent component representations for face recognition”, In: Rogowitz T, Pappas B, editors. Proc. SPIE Symp. Electon. Imaging: Science Technology —Human Vision and Electronic Imaging III; San Jose, CA, 1998, pp. 528–539.
23. M.S.Bartlett, J.R.Movellan, T.J.Sejnowski, “Face recognition by independent component analysis”. IEEE Transactions on neural networks, 2002, 13(6), 1450-1464.
24. B.A.Draper, K. Baek, M.S. Bartlett, J.R. Beveridge, “Recognizing faces with PCA and ICA:, Computer vision and image understanding, 2003, 91(1), 115-137.
25. Dagher, I., & Nachar, R. (2006). Face recognition using IPCAICA algorithm. IEEE transactions on pattern analysis and machine intelligence, 28(6), 996-1000.
26. C.Zhou, X.Wei, Q.Zhang, B.Xiao, “Image reconstruction for face recognition based on fast ICA”, International Journal of Innovative Computing, Information and Control, 2008, 4(7), 1723-1732.
27. J.Wright, A.Y.Yang, A.Ganesh, S.S.Sastry, Y.Ma, “Robust face recognition via sparse representation”, IEEE transactions on pattern analysis and machine intelligence, 2009, 31(2), 210-227.
28. J.Wright, Y.Ma, “Dense error correction via-minimization”, IEEE Transactions on Information Theory, 2010, 56(7), 3540- 3560.J.
29. M.Yang, L.Zhang, “ Gabor feature based sparse representation for face recognition with gabor occlusion dictionary”, In European conference on computer vision , 2010, (pp. 448- 461). Springer Berlin Heidelberg.
30. M.Yang, L.Zhang, J.Yang, D.Zhang, “Robust sparse coding for face recognition”, In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 2011,(pp. 625-632). IEEE.
31. A.Wagner, J.Wright, A.Ganesh, Z.Zhou, H.Mobahi, Y.Ma, “Toward a practical face recognition system: Robust alignment and illumination by sparse representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(2), 372- 386.
32. C.Y.Lu, H.Min, J.Gui, L.Zhu, Y.K.Lei, “Face recognition via weighted sparse representation”, Journal of Visual Communication and Image Representation, 2013, 24(2), 111- 116.
33. J.Wang, C.Lu, M.Wang, P.Li, S.Yan, X.Hu, “Robust face recognition via adaptive sparse representation”, IEEE transactions on cybernetics, 2014, 44(12), 2368-2378.
34. A. Adamo, G. Grossi, R.Lanzarotti, J.Lin, “Robust face recognition using sparse representation in LDA space”, Machine Vision and Applications, 2015, 26(6), 837-847.
35. T.Uiboupin, P.Rasti, G.Anbarjafari, H.Demirel, “Facial image super resolution using sparse representation for improving face recognition in surveillance monitoring”, In Signal Processing and Communication Application Conference (SIU), 2016 24th (pp. 437-440). IEEE.
36. A.S.M.Sohail, P.Bhattacharya, “Classification of facial expressions using k-nearest neighbor classifier”, In International Conference on Computer Vision/Computer Graphics Collaboration Techniques and Applications, 2007,(pp. 555-566). Springer Berlin Heidelberg.
37. Dhriti, and M.Kaur, “K-Nearest Neighbor Classification Approach for Face and Fingerprint at Feature Level Fusion”, International Journal of Computer Applications, 2012, 60(14).
38. P. Kamencay, M.Zachariasova, R.Hudec, R.Jarina, M.Benco, J.Hlubik, “A novel approach to face recognition using image segmentation based on spca-knn method”, Radioengineering, 2013, 22(1), 92-99.
39. P.Kasemsumran, S.Auephanwiriyakul, N.Theera-Umpon, “Face recognition using string grammar fuzzy K-nearest neighbor”. In 2016 8th International Conference on Knowledge and Smart Technology (KST), 2016, (pp. 55-59). IEEE.
40. J.Kim, J.Choi, J.Yi , M.Turk “Effective representation using ICA for face recognition robust to local distortion and partial occlusion”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(12):1977-81.
41. J.M.Keller, M.R.Gray, J.A.Givens, “A fuzzy k-nearest neighbor algorithm”, IEEE transactions on systems, man, and cybernetics, 1985, (4), 580-585.

Keywords
Local Binary Pattern (LBP), Independent Component Analysis (ICA), Sparse Representation, MRLSR, KNN.