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A Robust Biometric Image Texture Descripting Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 53 - Number 3 |
| Year of Publication: 2012 |
| Authors: J Bhattacharya, G Sanyal, S Majumder |
10.5120/8403-2466
|
J Bhattacharya, G Sanyal, S Majumder . A Robust Biometric Image Texture Descripting Approach. International Journal of Computer Applications. 53, 3 ( September 2012), 30-36. DOI=10.5120/8403-2466
Abstract
The huge demand in online transactions calls for a secure, safe and accurate authentication system . The biometric system such as face, iris, fingerprint, gait has already replaced the existing manual inspection process and surveillance systems in many disciplines. Amongst all these biometrics, face is more attractive as it provides information such as identity, expression, gender, ethnicity and age of an individual. Specially for surveillance purposes,the data acquisition for face is much more simpler and can be obtained without the subjects knowledge and cooperation (simply by installing camera in public areas) when compared to fingerprints and iris data accumulation. In this paper an edge texture feature using different weight mask coding is utilized for face recognition. Five different sign and difference operators named LSH LC, LSH GC,LDH LC, LDH GC and LSH LGV are developed and used to code the image texture feature. Each image is decomposed into four subset images which are used to generate the texture features. A decision fusion technique is then used for feature classification. The main advantage of this approach lies in the fact that they are computationally inexpensive when compared to most texture descriptors. The feature descriptor is applied for face biometric recognition to demonstrate the effectiveness of each approach in extracting textural features. It can also be tested with medical images or other pattern recognition applications. The dataset used for training and testing have considerable variances in lighting,viewpoint and other factors so that the potential of the feature extractor, when subjected to any kind of variations, can be judged.
References
- S. Tamura, H. Kawa, and H. Mitsumoto, "Male/Female identification from 8x6 very low resolution face images by neural network," Pattern Recognition, vol. 29, pp. 331-335, 1996.
- R. Bruneli and T. Poggio, "Face recognition: features versus templates," IEEE Transactions Pattern Analysis and Machine Intelligence, vol. 15, pp. 1042-1052, 1993.
- N. Jamil, S. Iqbal, N. Iqbal, "Face Recognition Using Neural Networks," IEEE Proc. of INMIC technology for the 21st century, pp. 277-281, 2001.
- P. J. Phillips, "Support vector machines applied to face recognition," Advances in Neural Information Processing Systems 11, MIT Press, 1999.
- Bhaskar Gupta , Sushant Gupta , Arun Kumar Tiwari,Face Detection Using Gabor Feature Extraction and Artificial Neural Network
- M. Turk and A. Pentland, "Eigenfaces for recognition," Journal Cognitive Neuroscience, vol. 3, pp. 71-86, 1991.
- Martinez, A. M. , Kak, A. C. , "PCA versus LDA," IEEE Transactions Pattern Analysis and Machine Intellgence Vol. 23 (2), pp. 228-233, 2001.
- M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, Face Recognition by Independent Component Analysis, IEEE Transaction on Neural Networks, Vol 13, pp. 1450-1464, 2002.
- Xiaoyang Tan and Bill Triggs,Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition,Springer Verlag, 2007
- YUCHUN FANG, ZHAN WANG,IMPROVING LBP FEATURES FOR GENDER CLASSIFICATION, Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, Hong Kong
- C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data mining and knowledge discovery, Vol. 2, pp. 121-167, 1998.
- Guoqin Cui, Wen Gao, Feng Jiao, and Shiguang Shan, "Face Recognition Based on Support Vector Method," Asian Conference on Computer Vision, January 2002.
- M. J. Nassiri and A. Vafaei and A. Monadjemi and Pwaset, " Texture Feature Extraction Using Slant-Hadamard Transform", World Academy Of Science, Engineering And Technology, 2006.
- Yuxin Liu and Yanda Li, " Image Feature Extraction And Segmentation Using Fractal Dimension",IEEE Conference on Information, Communications and Signal Processing, 1997.
- Hua Yuan and Xiao-Ping Zhang and Ling Guan, " A Statistical Approach For Image Feature Extraction In The Wavelet Domain",IEEE Conference on Electrical and Computer Engineering, 2003.
- Mona Sharma,Markos Markou,Sameer Singh, " Evaluation of Texture Methods for Image Analysis",Pattern Recognition Letters.
- Cootes, T. F. , Edwards, G. J. , Taylor, C. J. , "Active Appearance Models," Proc. European Conf. on Computer Vision, Vol. 2, pp. 484-498, Springers, 1998.
- Yi Liu,Tao Sun , Huang Yang , Yongmi Yang , Xinhong Zhou ,Wavelet-Based Face Recognition Method by Using Support Vector Machine,Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference, 2009
- Cunjian. chen Jiashu. zhang," Wavelet Energy Entropy as a New Feature Extractor for Face Recognition", Fourth International Conference on Image and Graphics,IEEE ,2007.
- Chengjun Liu, HarryWechsler, "A Gabor Feature Classifier for Face Recognition," Proceedings of ICCV, pp. 270-275, 2001.
- T. Ojala, M. Pietikinen, T. Menp, "Multiresolution grayscale and rotation invariant texture classification with local binary patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 971-987, 2002.
- T. Ahonen, A. Hadid, and M. Pietikinen, "Face Recognition with Local Binary Patterns," Proc. Eighth European Conf. Computer Vision, pp. 469-481, 2004.
- T. Ojala, M. Pietikinen, T. Menp, "Multiresolution grayscale and rotation invariant texture classification with local binary patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 971-987, 2002.
- CMU,PIE Face Database,http : ==www:ri:cmu:edu=researchprojectdetail:html
- F. Samaria and F. Fallside, "Face identification and feature extraction using hidden markov models," Image Processing: Theory and Application, Elsevier, 1993.
- S. Park and J. K. Aggarwal, "A Hierarchical Bayesian Network For Event Recognition Of Human Actions And Interactions" Multimedia System, 2004.
- S. Avidan, "Support Vector Tracking," In IEEE Conference On Computer Vision And Pattern Recognition (Cvpr), 2001.
- Zhenhua Guo, Lei Zhang, and David Zhang, " A Completed Modeling of Local Binary Pattern Operator for Texture Classification",IEEE Transactions on Image Processing. 36
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