Abstract
It is well recognized that image representation is the most fundamental task of the face recognition, effective and efficient image feature extraction not only has small intraclass variations and large interclass similarity but also robust to the impact of pose, illumination, expression and occlusion. This paper proposes a new local image descriptor for face recognition, named Log–Gabor Weber descriptor (LGWD). The idea of LGWD is based on the image Log-Gabor wavelet representation and the Weber local binary pattern(WLBP) features. The main motivation of the LGWD is to enhance the multiple scales and orientations Log-Gabor magnitude and phase feature by applying the WLBP coding method. Histograms extracted from the encoded magnitude and phase images are concatenated into one to form the image description finally. The experimental results on the ORL, Yale and UMIST face database verify the representation ability of our proposed descriptor.
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References
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997)
Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Trans. Neural Networks 13, 1450–1464 (2002)
Lu, J., Tan, Y.: Regularized locality preserving projections and its extensions for face recognition. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 40, 958–963 (2010)
Li, X., Lin, S., Yan, S., Xu, D.: Discriminant Locally Linear Embedding With High-Order Tensor Data. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 38, 342–352 (2008)
Yan, S., Xu, D., Zhang, B., Zhang, H., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29, 40–51 (2007)
Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Process. 11, 467–476 (2002)
Wang, W., Li, J., Huang, F., Feng, H.: Design and implementation of Log-Gabor filter in fingerprint image enhancement. Pattern Recogn. Lett. 29, 301–308 (2008)
Cline, M.T., Bernard, G.: Character segmentation-by-recognition using log-gabor filters. In: International Conference on Pattern Recognition, pp. 901–904 (2006)
Gao, X., Sattar, F., Venkateswarlu, R.: Multiscale corner detection of gray level images based on Log-Gabor wavelet transform. IEEE Trans. Circuits Syst. Video Technol. 17, 868–875 (2007)
Chen, X., Jing, Z.: Infrared face recognition based on Log-Gabor wavelets. Int. J. Pattern Recognit. Artif. Intell. 20, 351–360 (2006)
Arrospide, J., Salgado, L.: Log-Gabor filters for image-based vehicle verification. IEEE Trans. Image Process. 22, 2286–2295 (2013)
Singh, R., Vatsa, M., Noore, A.: Textural feature based face recognition for single training images. Electron. Lett. 41, 640–641 (2005)
Liu, F., Tang, Z., Tang, J.: WLBP: weber local binary pattern for local image description. Neurocomputing 120, 325–335 (2013)
David, J.F.: Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A 4, 2379–2394 (1987)
Kovesi, P.: Image features from phase congruency. Videre: A J. Comput. Vis. Res. 1, 1–26 (1999)
Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: WLD: a robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1705–1720 (2010)
Jain, A.K.: Fundamentals of Digital Signal Processing. Prentice-Hall, Englewood Cliffs (1989)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary pattern. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)
Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: IEEE Workshop on Applications of Computer Vision, pp. 138–142 (1994)
Graham, D.B., Allinson, N.M.: Characterizing virtual eigensignatures for general purpose face recognition. In: Wechsler, H., Jonathon Phillips, P., Bruce, V., Fogelman Soulié, F., Huang, T.S. (eds.) Face recognition: from theory to applications. NATO ASI Series F, Computer and Systems Sciences, vol. 163, pp. 446–456. Springer, Heidelberg (1998)
Ahonen, T., Hadid, A., Pietikanen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28, 2037–2041 (2006)
Yang, M., Zhang, L., Shiu, S.C.K., Zhang, D.: Monogenic binary coding: an efficient local feature extraction approach to face recognition. IEEE Trans. Inf. Forensics Secur. 7, 1738–1751 (2012)
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Li, J., Sang, N., Gao, C. (2015). Log-Gabor Weber Descriptor for Face Recognition. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_39
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DOI: https://doi.org/10.1007/978-3-319-16628-5_39
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