Paper:
Improvement of Face Recognition with Gabor, PCA, and SVM Under Complex Illumination Conditions
Liyun Zhuang*,** and Yepeng Guan*,***,
*School of Communications and Information Engineering, Shanghai University
99 Shangda Road, Baoshan District, Shanghai 200444, China
**Faculty of Electronic and Information Engineering, Huaiyin Institute of Technology
No.1 Meicheng East Road, Huaian, Jiangsu 223003, China
***Key Laboratory of Advanced Displays and System Application, Ministry of Education
Shanghai, China
Corresponding author
Complex illumination condition is one of the most critical challenging problems for practical face recognition. However, numerous studies have had no effective solutions reported for full illumination variation of face images in the facial recognition research field. In order to effectively solve full illumination variation problem, we propose a novel approach for illumination normalization for facial images based on the enhanced contrast method of histogram equalization (HE) and fusion of illumination estimations (FOIE). Then, feature extraction is applied with consideration of both Gabor wavelet and principal component analysis methods to process illumination normalization. Next, a support vector machine classifier (SVM) is used for face classification. Experimental results show that superior performance can be obtained in the developed approach by comparisons with some state-of-the-arts.
- [1] W. Zhao, R. Chellappa, and P. J. Phillips, “Face recognition: A literature survey,” ACM Computing Surveys (CSUR), Vol.35, No.4, pp. 399-458, 2003.
- [2] M. Sharif, K. Ayub, and D. Sattar, “Enhanced and fast face recognition by hashing algorithm,” J. of Applied Research and Technology, Vol.10, No.4, pp. 607-617, 2012.
- [3] L. Ramírez-Valdez and R. Hasimoto-Beltran, “3D-facial expression synthesis and its application to face recognition systems,” J. of Applied Research and Technology, Vol.7, No.3, pp. 354-373, 2009.
- [4] T. Zhang, B. Fang, and Y. Y. Tang, “Topology preserving non-negative matrix factorization for face recognition,” IEEE Trans. on Image Processing, Vol.17, No.4, pp. 574-584, 2008.
- [5] P. J. Phillips, W. T. Scruggs, and A. J. O’Toole, “FRVT 2006 and ICE 2006 large-scale experimental results,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.32, No.5, pp. 831-846, 2010.
- [6] P. Vageeswaran, K. Mitra, and R. Chellappa, “Blur and illumination robust face recognition via set-theoretic characterization,” IEEE Trans. on Image Processing, Vol.22, No.4, pp. 1362-1372, 2013.
- [7] H. Han and X. Chen, “A comparative study on illumination preprocessing in face recognition,” Pattern Recognition, Vol.46, No.6, pp. 1691-1699, 2013.
- [8] M. Savvides and B. V. K. Kumar, “Illumination normalization using logarithm transforms for face authentication,” IEEE Int. Conf. Audio-and Video-Based Biometric Person Authentication, pp. 1055-1055, 2003.
- [9] S. Shan, W. Gao, and B. Cao, “Illumination normalization for robust face recognition against varying lighting conditions,” IEEE Int. Conf. Analysis and Modeling of Faces and Gestures, pp. 157-164, 2003.
- [10] X. Xie and K. M. Lam, “Face recognition under varying illumination based on a 2D face shape model,” Pattern Recognition, Vol.38, No.2, pp. 221-230, 2005.
- [11] J. A. Stark, “Adaptive image contrast enhancement using generalizations of histogram equalization,” IEEE Trans. on Image Processing, Vol.9, No.5, pp. 889-896, 2000.
- [12] P. H. Lee, W. S. Wu, and Y. P. Hung “Illumination compensation using oriented local histogram equalization and its application to face recognition,” IEEE Trans. on Image Processing, Vol.21, No.9, pp. 4280-4289, 2012.
- [13] X. Xie, W. S. Zheng, and J. Lai, “Normalization of face illumination based on large-and small-scale features,” IEEE Trans. on Image Processing, Vol.20, No.7, pp. 1807-1821, 2011.
- [14] X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” IEEE Trans. on Image Processing, Vol.19, No.6, pp. 1635-1650, 2010.
- [15] D. J. Jobson, Z. Rahman, and G. A. Woodell, “A multiscale retinex for bridging the gap between color images and the human observation of scenes,” IEEE Trans. on Image Processing, Vol.6, No.7, pp. 965-976, 1997.
- [16] W. Chen, M. J. Er, and S. Wu, “Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain,” IEEE Trans. on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol.36, No.2, pp. 458-466, 2006.
- [17] H. Wang, S. Li, and Y. Wang, “Face recognition under varying lighting conditions using self quotient image,” IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 819-824, 2004.
- [18] T. Chen, W. Yin, and X. S. Zhou, “Total variation models for variable lighting face recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.28, No.9, pp. 1519-1524, 2006.
- [19] Q. Li, W. Yin, and Z. Deng, “Image-based face illumination transferring using logarithmic total variation models,” The Visual Computer, Vol.26, No.1, pp. 41-49, 2010.
- [20] X. Xie, S. Shan, and X. Chen, “Fusing local patterns of gabor magnitude and phase for face recognition,” IEEE Trans. on Image Processing, Vol.19, No.5, pp. 1349-1361, 2010.
- [21] H. Hu, “Illumination invariant face recognition based on dual-tree complex wavelet transform,” IET Computer Vision, Vol.9, No.2, pp. 163-173, 2014.
- [22] A. Baradarani, Q. M. J. Wu, and M. Ahmadi, “An efficient illumination invariant face recognition framework via illumination enhancement and DD-DTCWT filtering,” Pattern Recognition, Vol.46, No.1, pp. 57-72, 2013.
- [23] Y. Cheng, Y. Hou, and C. Zhao, “Robust face recognition based on illumination invariant in nonsubsampled contourlet transform domain,” Neurocomputing, Vol.73, No.10, pp. 2217-2224, 2010.
- [24] Y. Zhou, S. T. Zhou, and Z. Y. Zhong, “A de-illumination scheme for face recognition based on fast decomposition and detail feature fusion,” Optics Express, Vol.21, No.9, pp. 11294-11308, 2013.
- [25] S. Nikan and M. Ahmadi, “Local gradient-based illumination invariant face recognition using local phase quantization and multi-resolution local binary pattern fusion,” IET Image Processing, Vol.9, No.1, pp. 12-21, 2014.
- [26] Y. Wu, Y. Jiang, and Y. Zhou, “Generalized Weber-face for illumination-robust face recognition,” Neurocomputing, Vol.136, pp. 262-267, 2014.
- [27] J. R. Tang and N. A. M. Isa, “Bi-histogram equalization using modified histogram bins,” Applied Soft Computing, Vol.55, pp. 31-43, 2017.
- [28] X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” IEEE Trans. on Image Processing, Vol.19, No.6, pp. 1635-1650, 2009.
- [29] X. Yuan, Y. Meng, and X. Wei, “Illumination normalization based on homomorphic wavelet filtering for face recognition,” J. of Information Science and Engineering, Vol.29, No.3, pp. 579-594, 2013.
- [30] C. N. Fan and F. Y. Zhang, “Homomorphic filtering based illumination normalization method for face recognition,” Pattern Recognition Letters, Vol.32, No.10, pp. 1468-1479, 2011.
- [31] T. Chen, W. Yin, and X. S. Zhou, “Total variation models for variable lighting face recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.28, No.9, pp. 1519-1524, 2006.
- [32] Y. Jin and Q. Q. Ruan, “Face recognition using Gabor-based improved supervised locality preserving projections,” Computing and Informatics, Vol.28, No.1, pp. 81-95, 2012.
- [33] M. Li, X. Yu, K. H. Ryu, S. Lee, and N. Theera-Umpon, “Face recognition technology development with Gabor, PCA and SVM methodology under illumination normalization condition,” Cluster Computing, Vol.21, Issue 1, pp. 1117-1126, 2017.
- [34] K. Kim, “Face recognition using principle component analysis,” IEEE Int. Conf. Computer Vision and Pattern Recognition, pp. 586-591, 1996.
- [35] S. K. Bhattacharyya and K. Rahul, “Face recognition by linear discriminant analysis,” Int. J. of Communication Network Security, Vol.2, No.2, pp. 31-35, 2013.
- [36] L. N. Do, H. J. Yang, and S. H. Kim, “A multi-voxel-activity-based feature selection method for human cognitive states classification by functional magnetic resonance imaging data,” Cluster Computing, Vol.18, No.1, pp. 199-208, 2015.
- [37] M. Alhussein, “Automatic facial emotion recognition using weber local descriptor for e-Healthcare system,” Cluster Computing, Vol.19, No.1, pp. 99-108, 2016.
- [38] X. Li and G. Chen, “Face recognition based on PCA and SVM,” IEEE Int. Conf. Photonics and Optoelectronics, pp. 1-4, 2012.
- [39] F. Bellakhdhar, K. Loukil, and M. Abid, “Face recognition approach using Gabor Wavelets, PCA and SVM,” Int. J. of Computer Science Issues, Vol.10, No.2, pp. 201-207, 2013.
- [40] A. F. Basha and G. S. B. Jahangeer, “Face gender image classification using various wavelet transform and support vector machine with various kernels,” Int. J. of Computer Science Issues, Vol.9, No.6, pp. 150-157, 2012.
- [41] Y. Cheng, Z. Li, and Y. Han, “A novel illumination estimation for face recognition under complex illumination conditions,” IEICE Trans. on Information and Systems, Vol.100, No.4, pp. 923-926, 2017.
- [42] J. Wang and J. Cheng, “Face recognition based on fusion of Gabor and 2DPCA features,” IEEE Int. Intelligent Signal Processing and Communication Systems, pp. 1-4, 2010.
- [43] C. MageshKumar, R. Thiyagarajan, and S. P. Natarajan, “Gabor features and LDA based face recognition with ANN classifier,” IEEE Int. Emerging Trends in Electrical and Computer Technology, pp. 831-836, 2011.
- [44] C. Liu and H. Wechsler, “Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition,” IEEE Trans. on Image Processing, Vol.11, No.4, pp. 467-476, 2002.
- [45] F. S. Samaria and A. C. Harter, “Parameterisation of a stochastic model for human face identification,” Proc. of the 2th Int. Applications of Computer Vision, pp. 138-142, 1994.
- [46] F. Jiao, W. Gao, and L. Duan, “Detecting adult image using multiple features,” IEEE Int. Conf. Info-tech and Info-net, pp. 378-383, 2001.
- [47] H. Qian, Y. Mao, and W. Xiang, “Recognition of human activities using SVM multi-class classifier,” Pattern Recognition Letters, Vol.31, No.2, pp. 100-111, 2010.
- [48] K. C. Lee, et al., “The Extended Yale Face Database B,” http://vision.ucsd.edu/iskwak/ExtYaleDatabase/ExtYaleB.html [accessed January 10, 2018]
- [49] W. Gao, B. Cao, S. Shan, D. Zhou, X. Zhang, and D. Zhao, “CAS-PEAL Face Database,” http://www.jdl.ac.cn/peal/index.html [accessed January 10, 2018]
- [50] J. Ding, C. Wen, and G. Li, “Locality sensitive batch feature extraction for high-dimensional data,” Neurocomputing, Vol.171, pp. 664-672, 2016.
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