kailash sharma, Ramchandra R. Manthalkar


Gabor wavelet has proved to be an effective tool in extracting important features from the face images. In this research work we are proposing a face recognition system which uses Gabor filter bank to create Gabor feature images. The Gabor feature images with their different orientation and scale has increased the feature dimensionality. To reduce these features and form the final feature descriptor a local approach based on the regional histogram formation is used. In this approach, the featured images are divided into different regions. The histogram of each region is calculated and concatenated to form the final feature descriptor. Chi square similarity measure is used for classification. The effectiveness of the algorithm is justified on the face images which have illumination variations and pose variations in it.


Gabor wavelet; histogram; down sampling; feature vector; face descriptor; similarity measure

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P. Sinha, B. Balas, Y. Ostrovsky, R. Russell, Face Recognition by Humans: 19 Results All Computer Vision Researchers Should Know About, Proceedings of the IEEE, Vol. 94, No. 11, November 2006, pp. 1948-1962

Linlin Shen & Li Bai, “A review on Gabor wavelets for Face Recognition”, Pattern Analysis Application, vol. 9, 2006, pp. 273-292

W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips, Face Recognition: A Literature Survey, ACM Computing Surveys, 2003, pp. 399-458

Turk, M.A., Pentland, A.P., Eigenfaces for recognition. JournaL of Cognitive Neuroscience 3, 1991, pp.71–86.

Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J., Eigenfaces vs. fisherfaces: Recognitionusingclassspecificlinearprojection. IEEE Transactions on pattern analysis and machine intelligence 19, 1997, 711–720.

Bartlett, M.S., Movellan, J.R., Sejnowski, T.J., 2002. Face recognition by independent component analysis. IEEE Transactions on neural networks 13, 1450–1464.

Di Huang, Caifeng Shan, Mohsen Ardabilian, Yunhong Wang, and Liming Chen,"Local Binary Patterns and Its Application to Facial Image Analysis: A Survey", IEEE Trans.ON SYSTEMS, MAN, AND CYBERNETICS-PART C: APPLICATIONS AND REVIEWS, Vol. 41, No. 6, November 2011, pp. 765-781

Xiaoyang Tan and Bill Triggs, “Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 6, JUNE 2010, pp. 1635-1650

Zhang, Y. Gao, S. Zhao, and J. Liu, "Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor", IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 2, FEBRUARY 2010, pp.533-544

Adin Ramirez Rivera, Jorge Rojas Castillo, and Oksam Chae, “Local Directional Number Pattern for Face Analysis: Face and Expression Recognition”, IEEE Transaction on Image Processing, Vol. 22, No. 5, May 2013, pp. 1740-1752

Kuo-Chin Fan and Tsung-Yung Hung, “A Novel Local Pattern Descriptor—Local Vector Pattern in High-Order Derivative Space for Face Recognition”, IEEE Transactions on Image Processing, Vol. 23, No. 7, July 2014 pp. 2877-2891

Lades, M., Distortion invariant object recognition in the dynamic link architecture. IEEE Trans on Computers 42, 1997, pp. 300–311

Wiskott, J.-M., Fellous, N. Kruger, C.D. Von Malsburg, Face Recognition by Elastic Bunch Graph Matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, July 1997, pp. 775-779

Jie Zou, Qiang Ji, and George Nagy, "A Comparative Study of Local Matching Approach for Face Recognition", IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 16, NO. 10, OCTOBER 2007,pp. 2617-2628

J. Ruiz-del-Solar, P. Navarrete, Eigenspace-based face recognition: a comparative study of different approaches, IEEE Transactions on Systems, Man and Cybernetics, Part C, Vol. 35, Issue 3, August 2005, pp. 315-325

Kyrki, V., Kamarainen, J.K., Kalviainen, H., Simple Gabor feature space for invariant object recognition, Pattern Recognition Letters, 25, 2004, pp. 311-318

Ferdinando Samaria, Andy Harter,” Parameterisation of a Stochastic Model for Human Face Identification”, Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL, December 1994

The Extended Yale B database, http://vision.ucsd.edu/~iskwak/ExtYaleDatabase/ExtYaleB.html

DOI: https://doi.org/10.26483/ijarcs.v8i9.5126


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