In this paper, we introduce a novel discriminant technique called "exponential LDE" (ELDE) for dimensionality reduction in Face recognition. The proposed ELDE can be seen as an extension of LDE framework in two directions . First, the proposed framework overcomes the SSS problem without discarding the discriminant information that was contained in the null space of the locality preserving scatter matrices associated with LDE. Second, the proposed ELDE is equivalent to transforming original data into a new space by distance diffusion mapping (similar to kernel-based nonlinear mapping), and then, LDE is applied in such a new space. As a result of diffusion mapping, the margin between samples belonging to different classes is enlarged, which is helpful in improving classification accuracy. Identifying faces with facial expressions is also a challenging task, due to the deformation caused by the facial expressions . To solve these issues, a preprocessing step was carried out after which Blur and Illumination-Robust Face recognition algorithm was performed. The test image and training images with facial expression are transformed to neutral face using Facial expression removal (FER) operation. Every training image is transformed based on the optimal Transformation Spread Function (TSF), and illumination coefficients. Local BinaryPattern (LBP) features extracted from test image and transformed training image is used for classification.
Published In:IJCSN Journal Volume 8, Issue 3
Date of Publication : June 2019
Pages : 311-321
Tables : 01
Poonam Tulsiram Bawankar :
M.E. (W.C.C.) IVth sem, CSE Deptt., G. H. Raisoni Institute of Engineering and Technology,
RTMNU, Nagpur, Maharashtra, India-440028.
Hemlata Dakhore :
Assistant Professor, CSE department, G. H. Raisoni Institute of Engineering & Technology,
RTMNU, Nagpur, Maharashtra, India-440028.
Discriminant analysis, face recognition, featureextraction, graph-based embedding, local discriminant embedding (LDE), small-sample-size (SSS) problem
A robust face recognition system for unconstrained environment was developed using ELDE algorithm. In this algorithm, LBPfeatures were extracted for the blurred, illuminated, expressionvariated probe image. Every image in the gallery set wastransformed using optimal TSF and their LBP features wereextracted. A simple pre-processing step, FER was carried outand the reconstructed face images have been used for furtherprocessing. LBP features of transformed image and blurredprobe image were compared to find the best match.It was observed that for ELDE algorithm, when Cropped Yale was used, the recognition rate obtained was 81.986% and for Yale face dataset, 87.88%. It was observed that for ELDE used, the recognition rate noticed was 82% and for Yale facedataset, 67.996%. The system works effortlessly and is robustto conditions like blur, illumination and expressions. The results were improved when expression was removed.
 Fadi Dornaika and Alireza Bosaghzadeh," Exponential Local Discriminant Embedding and Its Application to Face Recognition", IEEE TRANSACTIONS ON CYBERNETICS, VOL. 43, NO. 3, JUNE 2013.
 L. Maaten, E. Postma, and J. Herik, "Dimensionality reduction: A comparative review," TiCC, Tilburg Univ., Tilburg, The Netherlands, TiCC TR 2009-005, 2009
 Anubha Pearline," Hemalatha. M ,"Face Recognition Under Varying Blur, Illumination and Expression in an Unconstrained Environment", Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500
 Abhijith Punnappurath and Ambasamudram Narayanan Rajagopalan, "Face Recognition Across Non-Uniform Motion Blur, Illumination, and Pose", IEEE Transactions On Image Processing, Vol. 13, No. 7, pp. 2067- 2082, Jul. 2015.
 L. Saul, K. Weinberger, F. Sha, J. Ham, and D. Lee, "Spectral methods for dimensionality reduction," in Semisupervised Learning. Cambridge, MA: MIT Press, 2006.
 S. Yan, D. Xu, B. Zhang, H. Zhang, Q. Yang, and S. Lin, "Graph embedding and extension: A general framework for dimensionality reduction," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 1, pp. 40-51, Jan. 2007.
 T. Zhang, D. Tao, X. Li, and J. Yang, "Patch alignment for dimensionality reduction," IEEE Trans. Knowl. Data Eng., vol. 21, no. 9, pp. 1299-1313, Sep. 2009.
 A. Jain, R. Duin, and J. Mao, "Statistical pattern recognition: A review," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 1, pp. 4-37, Jan. 2000.
 S. Roweis and L. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, no. 5500, pp. 2323-2326, Dec. 2000.
 M. Belkin and P. Niyogi, "Laplacian eigenmaps for dimensionality reduction and data representation," Neural Comput., vol. 15, no. 6, pp. 1373-1396, Jun. 2003.
 J. B. Tenenbaum, V. de Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol. 290, no. 5500, pp. 2319-2323, Dec. 2000.
 X. Li, S. Lin, S. Yan, and D. Xu, "Discriminant locally linear embedding with high-order tensor data," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 38, no. 2, pp. 342-352, Apr. 2008.
 A. M. Martinez and M. Zhu, "Where are linear feature extraction methods applicable?" IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 12, pp. 1934-1944, Dec. 2005
 H. Li, T. Jiang, and K. Zhang, "Efficient and robust feature extraction by maximum margin criterion," IEEE Trans. Neural Netw., vol. 17, no. 1, pp. 157-165, Jan. 2006.
 T. Kim and J. Kittler, "Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 3, pp. 318- 327, Mar. 2005.
 K. Fukunaga, Introduction to Statistical Pattern Recognition. New York: Academic, 1990.
 Y. Tao and J. Yang, "Quotient vs. difference: Comparison between the two discriminant criteria," Neurocomputing, vol. 18, no. 10-12, pp. 1808- 1817, Jun. 2010.  Y. Su, S. Shan, X. Chen, and W. Gao, "Classifiability-based discriminatory projection pursuit," IEEE Trans. Neural Netw., vol. 22, no. 12, pp. 2050-2061, Dec. 2011.
 D.-Q. Dai and P. C. Yuen, "Face recognition by regularized discriminant analysis," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 37, no. 4, pp. 1080-1085, Aug. 2007.
 J. Lu, K. Plataniotis, and A. Venetsanopoulos, "Face recognition using kernel direct discriminant analysis algorithms," IEEE Trans. Neural Netw., vol. 14, no. 1, pp. 117-126, Jan. 2003.
 M. Sugiyama, "Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis," J. Mach. Learn. Res., vol. 8, no. 5, pp. 1027-1061, May 2007.
 S. Yan, D. Xu, B. Zhang, and H.-J. Zhang, "Graph embedding: A general framework for dimensionality reduction," in Proc. Int. Conf. Comput. Vis Pattern Recognit., 2005, pp. 830-837.
 X. He and P. Niyogi, "Locality preserving projections," in Proc. Conf.Adv. Neural Inf. Process. Syst., Vancouver, BC, Canada, 2003.
21] X. He, S. Yan, Y. Hu, P. Niyogi, and H.-J. Zhang, "Face recognition using Laplacianfaces," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 3, pp. 328-340, Mar. 2005.
 Y. Xu, A. Zhong, J. Yang, and D. Zhang, "LPP solution schemes for use with face recognition," Pattern Recognit., vol. 43, no. 12, pp. 4165-4176, Dec. 2010.
 T. Zhang, B. Fang, Y. Tang, Z. Shang, and B. Xu, "Generalized discriminant analysis: A matrix exponential approach," IEEE Trans. Syst., Man,Cybern. B, Cybern., vol. 40, no. 1, pp. 186-197, Feb. 2010.  Y. Fu, Z. Li, J. Yuan, Y. Wu, and T. S. Huang, "Locality versus globality: Query-driven localized linear models for facial image computing," IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 12, pp. 1741-1752, Dec. 2008.
 W. Yu, X. Teng, and C. Liu, "Face recognition using discriminant locality preserving projections," Image Vis. Comput., vol. 24, no. 3, pp. 239-248, Mar. 2006.
 W. Wong and H. Zhao, "Supervised optimal locality preserving projection," Pattern Recognit., vol. 45, no. 1, pp. 186-197, Jan. 2012.
 F. Wang, X. Wang, D. Zhang, C. Zhang, and T. Li, "Marginface: A novel face recognition method by average neighborhood margin maximization," Pattern Recognit., vol. 42, no. 11, pp. 2863-2875, Nov. 2009.
 B. Alipanahi, M. Biggs, and A. Ghodsi, "Distance metric learning vs. Fisher discriminant analysis," in Proc. AAAI Conf. Artif. Intell., 2008, pp. 598-603.
 A. Globerson and S. Roweis, "Metric learning by collapsing classes," in Proc. Conf. Adv. Neural Inf. Process. Syst., 2006, pp. 451-458.
 K. Q. Weinberger and L. K. Saul, "Distance metric learning for large margin nearest neighbor classification," J. Mach. Learn. Res., vol. 10, pp. 207-244, Dec. 2009.
 E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell, "Distance metric learning with application to clustering with side-information," in Advances in Neural Information Processing Systems, vol. 15. Cambridge, MA: MIT Press, 2003, pp. 505-512.
 H. Chen, H. Chang, and T. Liu, "Local discriminant embedding and its variants," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., 2005, pp. 846-853.
 S.Wang, H. Chen, X. Peng, and C. Zhou, "Exponential locality preserving projections for small sample size problem," Neurocomputing, vol. 74, no. 17, pp. 3654-3662, Oct. 2011.  B. Schölkopf and A. Smola, Learning With Kernels. Cambridge, MA: MIT Press, 2002.
 B. Schölkopf, A. Smola, and K.Müller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Comput., vol. 10, no. 5, pp. 1299-1319, Jul. 1998.
 S. Mika, G. Rätsch, B. Schölkopf, A. Smola, J. Weston, and K. Müller, "Invariant feature extraction and classification in kernel spaces," in Proc.Adv. Neural Inf. Process. Syst., 1999, pp. 526-532.
 G. Baudat and F. Anouar, "Generalized discriminant analysis using a kernel approach," Neural Comput., vol. 12, no. 10, pp. 2385-2404, Oct. 2000.
 L. Chen, H. Liao, J. Lin, M. Kao, and G. Yu, "A new LDA-based face recognition system which can solve the small sample size problem," Pattern Recognit., vol. 33, no. 10, pp. 1713-1726, Oct. 2000.
 J. Yang, A. F. Frangi, D. Z. J.-Y. Yang, and Z. Jin, "KPCA plus LDA: A complete kernel Fisher discriminant framework for feature extraction and recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 2, pp. 230-244, Feb. 2005.
 D. Cai, X. He, K. Zhou, J. Han, and H. Bao, "Locality sensitive discriminant analysis," in Proc. Int. Joint Conf. Artif. Intell., 2007, pp. 708-713.
 Y. Fu, M. Liu, and T. S. Huang, "Conformal embedding analysis with local graph modeling on the unit hypersphere," in Proc. IEEE Int. Conf.Comput. Vis. Pattern Recognit., 2007, pp. 1-6.
 H. Yu and H. Yang, "A direct LDA algorithm for high-dimensional data: With application to face recognition," Pattern Recognit., vol. 34, no. 10, pp. 2067-2070, Oct. 2001.
 C. Moler and C. V. Loan, "Nineteen dubious ways to compute the exponential of a matrix, twenty-five years later," SIAM Rev., vol. 45, no. 1, pp. 3-49, Mar. 2003.
 N. Higham, "The scaling and squaring method for the matrix exponential revisited," SIAM J. Matrix Anal. Appl., vol. 26, no. 4, pp. 1179-1196, 2005.
 J. Shawe-Taylor and N. Cristianini, Support Vector Machines and OtherKernel-Based Learning Methods. Cambridge, U.K.: Cambridge Univ.Press, 2000.
 X. Chai, S. Shan, X. Chen, and W. Gao, "Locally linear regression for pose-invariant face recognition," IEEE Trans. Image Process., vol. 16, no. 7, pp. 1716-1725, Jul. 2007.
All rights reserverd @ IJCSN International Journal www.IJCSN.org