DFT domain Feature Extraction using Edge-based Scale Normalization for Enhanced Face Recognition
Abstract
This paper proposes a novel preprocessing technique in order to improve the performance of a Face Recognition (FR) system. The proposed Edge-based Scale Normalization (ESN) process involves the use of scale normalization along with edge detection as a preprocessing technique in order to eliminate unwanted background details in face images. Feature extraction is performed on the preprocessed image using Discrete Fourier Transform (DFT). The DFT spectrums of these images extract the low frequency coefficients required for face recognition. These important features are selected through a rhombus-shaped mask around the center of the DFT spectrum. Further optimization in feature selection is achieved through Binary Particle Swarm Optimization (BPSO) technique. Experimental results, obtained by applying the proposed algorithm on Cambridge ORL and Extended YaleB face databases, show that the proposed system outperforms other FR systems. A significant increase in the recognition rate and a substantial reduction in the number of features are observed. Significant dimensionality reduction by more than 98.5% and improved recognition rate of 98% are achieved for both datasets.
About this article
References
[1] W. Zhao, R. Chellappa, P.J. Phillips, A. Rosenfeld, Face recognition: A literature survey, ACM Computing Surveys 35 (2003) 399-458.
[2] Xuan Zou, J. Kittler, K. Messer, Illumination invariant face recognition: A survey, First IEEE International Conference on Biometrics: Theory, Applications, and Systems (2007).
[3] A.M. Patil, S.R. Kolhe, P.M. Patil, 2D Face recognition techniques: A survey, International Journal of Machine Intelligence 2 (2010) 74-83.
[4] Cha Zhang, Zhengyou Zhang, A survey of recent advances in face detection, Technical Report MSR-TR-2010-66 (2010).
[5] G. Shakhnarovich, B. Moghaddam, Face recognition in subspaces, Chapter 2, Handbook of Face Recognition, Springer-Verlag London Limited, 2011; 19-49.
View more references (35)
[6] Chris Ding, Hanchuan Peng, Minimum redundancy feature selection from microarray gene expression data, Journal of Bioinformatics and Computational Biology 3 (2005) 185-205.
[7] O. Sigaud, S.W. Wilson, Learning classifier systems: A survey, Soft Computing 11 (2007) 1065-1078.
[8] Jianxin Wu, James M. Rehg, Beyond the Euclidean distance: Creating effective visual codebooks using the histogram intersection kernel, Proceedings of IEEE International Conference on Computer Vision (2005).
[9] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, Third Edition, Prentice Hall, 2008; 133-135, 242-275, 485-488. 164 K Manikantan, S Ramachandran
[10] D.J. Beymer, Face recognition under varying pose, Proceedings of IEEE International conference on Computer Vision and Pattern Recognition (1994) 756-761.
[11] D. Reisfeld, N. Arad, Y. Yeshurn, Normalization of face mages using few anchors, Proceedings of International conference on pattern recognition (1994) 761-763.
[12] M. Kirby, L. Sirovich, Application of the Karhunen-Loeve procedure for the charaterization of human faces, IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1990) 103-108.
[13] Matthew A. Turk, Alex P. Pentland, Face recognition using eigenfaces, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1991).
[14] L.D. Harmon, The recognition of faces, Scientific American 229 (1973) 71-82.
[15] A.P. Ginsburg, Visual information processing based on spatial filters constrained by biological data, AMRL Technical Report (1991).
[16] J.H. Lail, P.C. Yuen, G.C. Feng, Face recognition using holistic Fourier invariant features, Pattern Recognition 34 (2001) 95-109.
[17] Jingdong Wang, Changshui Zhang, Heung-Yeung Shum, Face image resolution versus face recognition performance based on two global methods, Proceedings of Asia Conference on Computer Vision (2004).
[18] B.J. Boom, G.M. Beumer, L.J. Spreeuwers, R.N.J. Veldhuis, The effect of image resolution on the performance of a face recognition system, Proceedings of International Conference on Control, Automation, Robotics and Vision, ICARCV (2006).
[19] Mark S. Nixon, Alberto S. Aguado, Feature extraction and image processing, Second Edition, Academic Press publications, Elsevier Ltd, 2008.
[20] J. Kennedy, R. C. Eberhart, A discrete binary version of the particle swarm algorithm, Proceedings of World Multiconference on Systemics, Cybernetics and Informatics (1997) 4104-4109.
[21] Xu Jun, Huiyou Chang, The discrete binary version of the improved Particle swarm optimization algorithm, Proceedings of International Conference on Management and Service Science (2009) 1-6. DFT domain Feature Extraction using Edge-based Scale Normalization for Enhanced Face Recognition 165
[22] C. Liu, H. Wechsler, Evolutionary pursuit and its application to face recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000) 570-582.
[23] Xiaohui Hu, Yuhui Shi, R. C. Eberhart, Recent advances in particle swarm, Proceedings of IEEE Congress on Evolutionary Computation (2004).
[24] Maurice Clerc, Binary particle swarm optimisers: toolbox, derivations, and mathematical insights, Version 1, (2007).
[25] J. Kennedy, R. C. Eberhart, Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks 4 (1995) 1942-1948.
[26] Guimin Chen, Jianyuan Jia, Qi Han, Geometrical profile optimization of elliptical flexure hinge using a modified particle swarm algorithm, c Springer-Verlag Berlin Heidelberg 1 (2005) 533-542.
[27] ORL Database: <http://www.cl.cam.ac.uk/research/dtg/ attarchive/facedatabase.html>.
[28] Yale Univ. Face Database <http://cvc.yale.edu/projects/ yalefaces/yalefaces.html>.
[29] Kuang-Chih Lee, Jeffrey Ho, David J. Kriegman, Acquiring linear subspaces for face recognition under variable lighting, IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (2005).
[30] A S. Georghiades, P N. Belhumeur, D.J. Kriegman, From few to many: Illumination cone models for face recognition under variable lighting and pose, IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (2001) 643-660.
[31] Shiguang Shan, Wen Gao, Bo Cao, Debin Zhao, Illumination normalization for robust face recognition against varying lighting conditions, Analysis and Modeling of faces and gestures, IEEE International Workshop (2003).
[32] K. Rama Linga Reddy, G.R. Babu, Lal Kishore, Face recognition based on Eigen features of multi scaled face components and an artificial neural network, Proceedings of International Conference and Exhibition on Biometrics Technology, Procedia Computer Science 2 (2010) 62-74.
[33] Yulian Zhu, Jun Liu, Songcan Chen, Y. Zhu, Semi-random subspace method for face recognition, Image and Vision Computing 27 (2009) 1358-1370.
166 K Manikantan, S Ramachandran
[34] M. Yang, Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods, Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition (2002).
[35] H F. Chen, P.N. Belhumeur, D.W. Jacobs, In search of illumination invariants, IEEE Conference on Computer Vision and Pattern Recognition 1 (2000) 254-261.
[36] B.V.K.V. Kumar, M. Savvides, C. Xie, Correlation pattern recognition for face recognition, Proceedings of IEEE 94 (2006) 1963-1976.
[37] Jian Yang, David Zhang, Alejandro F. Frangi, Jing-yu Yang, TwoDimensional PCA: A new approach to appearance-based face representation and recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (2004) 131-137.
[38] Raghuraman Gopalan, David Jacobs, Comparing and combining lighting insensitive approaches for face recognition, Computer Vision and Image Understanding 114 (2010) 135-145.
[39] <http://www.mathworks.com>.