International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 10, No. 6, 8 Jun. 2018
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Discrete Wavelet Transform based High Performance Face Recognition Using a Novel Statistical Approach
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Author(s)
Nazife Cevik
1
Taner Cevik
2,*
Index Terms
Face Recognition, Discrete Wavelet Transform, Gray-Level Co-occurrence Matrix, Gray-Level Total Displacement Matrix, Texture Extraction, Classification
Abstract
Biometrics has gained significant popularity for individual identification in the last decades as a necessity of supporting especially the law enforcement and personal authentication required applications. The face is one of the distinctive biometrics that can be used to identify an individual. Henceforth, Face Recognition (FR) has attracted the great interest of the scientists and academicians. One of the most popular methods preferred for FR is extracting textual features from face images and subsequently performing classification according to these features. A substantial portion of the previous texture analysis and classification studies have based on extracting features from Gray Level Co-occurrence Matrix (GLCM). In this study, we present an alternative method that utilizes Gray Level Total Displacement Matrix (GLTDM) which holds statistical information about the Discrete Wavelet Transform (DWT) of the original face image. The approximation and three detail sub-bands of the image are first calculated. GLTDMs that are specific to these four matrices are subsequently generated. The Haralick features are extracted from those generated four GLTDMs. At the following stage, a new joint feature vector is formed using these four groups of Haralick features. Lastly, extracted features are classified by using K-NN algorithm. As demonstrated in the simulation results, the proposed approach performs promising results in the context of classification.
Cite This Paper
Nazife Cevik, Taner Cevik, " Discrete Wavelet Transform based High Performance Face Recognition Using a Novel Statistical Approach ", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.6, pp. 1-9, 2018. DOI: 10.5815/ijigsp.2018.06.01
Reference
[1]A. K. Jain, A. Ross, S. Prabhakar, “An introduction to biometric recognition”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14 No. 1, 2004, pp. 4-20.
[2]D. Maltoni, D. Maio, A. K. Jain, S. Prabhakar, Handbook of Fingerprint Recognition. Springer-Verlag, 2009.
[3]A. Ali, X. Jing, N. Saleem, “GLCM-based fingerprint recognition algorithm”, in Proceedings of the 4th IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT), 2011, pp. 207-211.
[4]A. K. Jain, S. Pankanti, “Biometrics systems: anatomy of performance”, Ieice Transactions on Fundamentals of Electronics Communications and Computer Sciences, E00-A (1), 2001, pp. 1-11.
[5]S. B. Nikam, S. Agarwal, “Wavelet energy signature and GLCM features-based fingerprint anti-spoofing”, in Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, 2008, pp. 717-723.
[6]R. Shyam, Y. N. Singh, “Identifying individuals using multimodal face recognition techniques”, Procedia Computer Science, Vol. 48, 2015, pp. 66-672.
[7]A. K. Jain, A. Ross, “Multibiometric systems”, Communications of the ACM, Vol. 47, No. 1, 2004, pp. 34-40.
[8]F. Purnomo, D. Suhartono, M. Shodiq, A. Susanto, S. Raharja, R. W. Kurniawan, “Face recognition using wavelet and non-negative matrix factorization”, in Proceedings of the SAI Intelligent Systems Conference, 2015, pp. 10-11.
[9]Z. H. Huang, W. J. Li, T. Zhang, “Face recognition based on pixel-level and feature-level fusion of the top-level’s wavelet sub-bands”, Vol. 22, 2015, pp. 95-104.
[10]Y. Cheng, Y. K. Hou, C. X. Zhao,, Z. Y. Li, Y. Hu, C. I. Wang, “Robust face recognition based on illumination invariant in nonsubsampled contourlet transform domain”, Neurocomputing, Vol. 73, No. 10-12, 2010, pp. 2217-2224.
[11]X. Z. Zhang, Y. S. Gao, “Face recognition across pose: a review”, Pattern Recognition, Vol. 42 No. 11, 2009, pp. 2876-2896.
[12]A. M. Martinez, “Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24 No. 6, 2002, pp. 748-763.
[13]K. Huang, S. Aviyente, “Wavelet Feature Selection for Image Classification”, IEEE Transactions on Image Processing, Vol. 17 No. 9, 2008, pp. 1709-1720.
[14]J. W. Zhao, Z. H. Zhou, F. L. Cao, “Human face recognition based on ensemble of polyharmonic extreme learning machine”, Neural Computing and Applications, Vol. 24 No. 6, 2014, pp. 1317-1326.
[15]P. N. Belhumeur, J. P. Hespanha, D. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19 No. 7, 1997, pp. 711-720.
[16]I. T. Jolliffe, “Principal Component Analysis”, Springer, New York, USA, 1986.
[17]T. Ahonen, A. Hadid, M. Pietikainen, “Face description with local binary pattern: Application to face recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28 No. 12, 2006, pp. 2037-2041.
[18]R. M. Haralick, K. Shanmugam, I. Dinstein, “Textural Features for Image Classification”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 3 No. 6, 1973, pp. 610-621.
[19]A. Laine, J. Fan, “Texture classification by wavelet signature”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15 No. 11, 1993, pp. 1186-1191.
[20]G. Fan, X. Xia, “Wavelet-based texture analysis and synthesis using hidden Marko model”, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, Vol. 50 No. 1, 2003, pp. 106-120.
[21]T. Chang, C. Kuo, “Texture analysis and classification with structured wavelet transform”, IEEE Transactions on Image Processing, Vol. 2 No. 4, 1993, pp. 429-441.
[22]G. Wouwer, P. Scheunders, D. Dyck, “Statistical texture characterization from discrete wavelet representations”, IEEE Transactions on Image Processing, Vol. 8 No. 4, 1999, pp. 592-598.
[23]C. Garcia, G. Zikos, G. Tziritas, “Wavelet packet analysis for face recognition”, Image and Vision Computing, Vol. 18 No. 4, 2000, pp. 289-297.
[24]M. Do, M. Vetterli, “Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden markov models”, IEEE Transactions on Multimedia, Vol. 4 No. 4, 2002, pp. 517-527.
[25]M. Do, M. Vetterli, “Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance”, IEEE Transactions on Image Processing, Vol. 11 No. 2, 2002, pp. 146-158.
[26]M. Acharyya, R. De, M. Kundu, “Extraction of feature using m-band wavelet packet frame and their neuro-fuzzy evaluation for multitexture segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25 No. 12, 2003, pp. 1639-1644.
[27]S. Arivazhagan, L. Ganesan, “Texture classification using wavelet transform”, Pattern Recognition Letters, Vol. 24 No. 10, 2003, pp. 1513-1521.
[28]F. R. Siqueira, W. R. Schwartz, H. Pedrini, “Multi-scale gray level co-occurrence matrices for texture description”, Neurocomputing, Vol. 120, 2013, pp. 336-345.
[29]W. Schwartz, H. Pedrini, “Texture image segmentation based on spatial dependence using a Markov random field model”, in Proceedings of the IEEE International Conference on Image Processing, 2006, pp. 2449-2452.
[30]D. Chen, C. M. Chang, A. Laine, “Detection and enhancement of small masses via precision multiscale analysis”, Lecture Notes in Computer Science, Vol. 1351, 2005, pp. 192-199.
[31]S. Arivazhagan, L. Ganesan, “Texture classification using wavelet transform”, Pattern Recognition Letters, Vol. 24, 2003, pp. 1513-1521.
[32]R. C. Gonzalez, R. E. Woods, Digital Image Processing. 3rd Ed., Prentice Hall, 2008.
[33]R. C. Gonzalez, R. E. Woods, S. L. Eddins, Digital Image Processing Using MATLAB, 2nd Ed., Gatesmark Publishing, 2009.
[34]C. G. Eichkitz, J. Davies, J. Amtmann, M. G. Schreilechner, P. Groot, “Grey level co-occurrence matrix and its application to seismic data”, First Break, Vol. 33 No. 3, 2015, pp. 71-77.
[35]M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, “Rock texture retrieval using gray level co-occurrence matrix”, in Proceedings of the 5th Nordic Signal Processing Symposium (NORSIG ’02), 2002, pp. 1-5.
[36]E. Miyamoto, T. Merryman, “Fast calculation of Haralick texture features”, Technical Report, Carnegie Mellon University, 2008.
[37]R. Jain, R. Kasturi, B. G. Schunck, Machine Vision. McGraw-Hill, 1995.
[38]A. Eleyan, H. Demirel, “Co-occurrence based statistical approach for face recognition”, in Proceedings of the 24th International Symposium on Computer and Information Sciences (ISCIS’09), 2009, pp. 611-615.
[39]A. Eleyan, H. Demirel, “Co-occurrence matrix and its statistical features as a new approach for face recognition”, Turkish Journal of Electrical & Computer Engineering, Vol. 19 No. 1, 2011, pp. 97-107.
[40]R. Polikar, “Fundamental concepts & an overview of the wavelet theory”, The Wavelet Tutorial 2nd ed PartI, http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf
[41]http://www.wavelet.org/tutorial/wbasic.htm
[42]D. Hazra, “Texture recognition with combined glcm wavelet and rotated wavelet features, International Journal of Computer and Electrical Engineering, Vol. 3 No. 1, 2011, pp. 1793-8163.
[43]A. C. Gonzalez-Garcia, J. H. Sossa-Azuela, E. M. Felipe-Riveron, O. Pogrebnyak, “Image retrieval based on wavelet transform and neural network classification”, Computación y Sistemas, Vol. 11 No. 2, 2007, pp. 143-156.
[44]G. Li, B. Zhou, Y. Su, “Face recognition algorithm using two dimensional locality preserving projection in discrete wavelet domain”, The Open Automation and Control Systems Journal, Vol. 7, 2015, pp. 1721-1728.
[45]J. Han, M. Kamber, Data mining concepts and techniques. Morgan Kaufmann Publishers, Burlington, 2006.
[46]M. R. Zare, W. C. Seng, A. Mueen, “Automatic classification of medical x-ray images”, Malaysian Journal of Computer Science, Vol. 26 No.1, 2013, pp. 9-22.
[47]L. Spacek, “Face Recognition Data”, 2000, http://cswww.essex.ac.uk/mv/allfaces/faces94.html