GENDER CLASSIFICATION FROM THE IRIS CODE USED FOR RECOGNITION
DOI:
https://doi.org/10.20894/IJMSR.117.009.001.027Keywords:
Recognition, LBP, SVM, rank features.Abstract
In this paper, we present a new method for gender classification based on features of the iris texture selected by mutual information to improve gender classification of face images. The performance of the proposed approach has been investigated through extensive experiments performed on public database. We also showed improved results by fusion of texture features with best features selected independently from the left and right irises based on selection of features using rank feature selection method. The classification task has been achieved by using Support Vector Machine (SVM). We compared our method with existing gender classification methods based on texture feature with classifier being the same as SVM.
Downloads
Download data is not yet available.
References
[1] Juan E. Tapia, Claudio A. Perez & Kevin W. Bowyer. Gender Classification from Iris Images Using Fusion of Uniform Local Binary Patterns, 10.1007/978-3-319-16181-5 57.
[2] Pavithra A, Saravanan P, Venkataraman N L,’ Gender Classification Based on Selecting Features LBP, Intensity, and Shape’, International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181, Vol. 3 Issue 3, March – 2014.
[3] Michael Fairhurst1, Meryem Erbilek2, Marjory Da Costa-Abreu. EXPLORING GENDER PREDICTION FROM IRIS BIOMETRICS. ©2015 Gesellschaft für Informatik e.V., Bonn, Germany.
[4] Atul Bansal &Ravinder Agarwal, R.K. Sharma. SVM Based Gender Classification Using Iris Images, 978-0-7695-4850-0/12 $26.00 © 2012 IEEE
[5] Ms.Bindhu K. Rajan, Ms.Nimpha Anto &Ms.Sneha Jose. Fusion of Iris & Fingerprint Biometrics for Gender Classification Using Neural Network.
[6] Khan, S. A., Nazir, M., Akram, S., & Riaz, N. (2011, December). Gender classification using image processing techniques: A survey. In Multitopic Conference (INMIC), 2011 IEEE 14th International (pp. 25-30).
[7] Thomas, V., Chawla, N. V., Bowyer, K. W., & Flynn, P. J. (2007, September). Learning to predict gender from iris images. BTAS. First IEEE International Conference on In Biometrics: Theory, Applications, and Systems, (pp. 1-5).
[8] Gawande, U., Hajari, K., & Golhar, Y. (2016, October). Novel technique for removing corneal reflection in noisy environment—Enhancing iris recognition performance. In Signal and Information Processing (IConSIP), International Conference on (pp. 1-5). IEEE.
[9] Hashim, A. T., & Noori, D. A. (2016, September). An Approach of Noisy Color Iris Segmentation Based on Hybrid Image Processing Techniques. In Cyberworlds (CW), 2016 International Conference on (pp. 183-188). IEEE.
[10] Hu, Y., Sirlantzis, K., & Howells, G. (2017). Optimal Generation of Iris Codes for Iris Recognition. IEEE Transactions on Information Forensics and Security, 12(1), 157-171.
[11] Balap, P., Khoje, S., & Pardeshi, P. (2017, January). A critical review on wavelet based feature extraction for Iris Recognition. In Intelligent Systems and Control (ISCO), 2017 11th International Conference on (pp. 235-240). IEEE.
[12] Minaee, S., Abdolrashidiy, A., & Wang, Y. (2016, December). An experimental study of deep convolutional features for iris recognition. In Signal Processing in Medicine and Biology Symposium (SPMB), 2016 IEEE (pp. 1-6). IEEE.
[13] Nalla, P. R., & Kumar, A. (2017). Toward More Accurate Iris Recognition Using Cross-Spectral Matching. IEEE Transactions on Image Processing, 26(1), 208-221.
[14] Salve, S. S., & Narote, S. P. (2016, March). Iris recognition using SVM and ANN. In Wireless Communications, Signal Processing and Networking (WiSPNET), International Conference on (pp. 474-478). IEEE.
[15] Thomas, V., Chawla, N. V., Bowyer, K. W., & Flynn, P. J. (2007, September). Learning to predict gender from iris images. In Biometrics: Theory, Applications, and Systems, 2007. BTAS 2007. First IEEE International Conference on (pp. 1-5). IEEE.
[2] Pavithra A, Saravanan P, Venkataraman N L,’ Gender Classification Based on Selecting Features LBP, Intensity, and Shape’, International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181, Vol. 3 Issue 3, March – 2014.
[3] Michael Fairhurst1, Meryem Erbilek2, Marjory Da Costa-Abreu. EXPLORING GENDER PREDICTION FROM IRIS BIOMETRICS. ©2015 Gesellschaft für Informatik e.V., Bonn, Germany.
[4] Atul Bansal &Ravinder Agarwal, R.K. Sharma. SVM Based Gender Classification Using Iris Images, 978-0-7695-4850-0/12 $26.00 © 2012 IEEE
[5] Ms.Bindhu K. Rajan, Ms.Nimpha Anto &Ms.Sneha Jose. Fusion of Iris & Fingerprint Biometrics for Gender Classification Using Neural Network.
[6] Khan, S. A., Nazir, M., Akram, S., & Riaz, N. (2011, December). Gender classification using image processing techniques: A survey. In Multitopic Conference (INMIC), 2011 IEEE 14th International (pp. 25-30).
[7] Thomas, V., Chawla, N. V., Bowyer, K. W., & Flynn, P. J. (2007, September). Learning to predict gender from iris images. BTAS. First IEEE International Conference on In Biometrics: Theory, Applications, and Systems, (pp. 1-5).
[8] Gawande, U., Hajari, K., & Golhar, Y. (2016, October). Novel technique for removing corneal reflection in noisy environment—Enhancing iris recognition performance. In Signal and Information Processing (IConSIP), International Conference on (pp. 1-5). IEEE.
[9] Hashim, A. T., & Noori, D. A. (2016, September). An Approach of Noisy Color Iris Segmentation Based on Hybrid Image Processing Techniques. In Cyberworlds (CW), 2016 International Conference on (pp. 183-188). IEEE.
[10] Hu, Y., Sirlantzis, K., & Howells, G. (2017). Optimal Generation of Iris Codes for Iris Recognition. IEEE Transactions on Information Forensics and Security, 12(1), 157-171.
[11] Balap, P., Khoje, S., & Pardeshi, P. (2017, January). A critical review on wavelet based feature extraction for Iris Recognition. In Intelligent Systems and Control (ISCO), 2017 11th International Conference on (pp. 235-240). IEEE.
[12] Minaee, S., Abdolrashidiy, A., & Wang, Y. (2016, December). An experimental study of deep convolutional features for iris recognition. In Signal Processing in Medicine and Biology Symposium (SPMB), 2016 IEEE (pp. 1-6). IEEE.
[13] Nalla, P. R., & Kumar, A. (2017). Toward More Accurate Iris Recognition Using Cross-Spectral Matching. IEEE Transactions on Image Processing, 26(1), 208-221.
[14] Salve, S. S., & Narote, S. P. (2016, March). Iris recognition using SVM and ANN. In Wireless Communications, Signal Processing and Networking (WiSPNET), International Conference on (pp. 474-478). IEEE.
[15] Thomas, V., Chawla, N. V., Bowyer, K. W., & Flynn, P. J. (2007, September). Learning to predict gender from iris images. In Biometrics: Theory, Applications, and Systems, 2007. BTAS 2007. First IEEE International Conference on (pp. 1-5). IEEE.
Downloads
Published
2017-03-27
Issue
Section
Articles
License
Authors need to sign following agreement with International Journal of MC Square Scientific Research before publishing their articles:
- Authors need to return copyright form to Journal Editor-in-chief to proceed their articles for publication. Meantime, the journal licensed under a Creative Commons Attribution License, which permits other user to distribute the work with an acknowledgement of the authors for International Journal of MC Square Scientific Research.
- Authors are also able to share their separate, additional contractual arrangements for the non-restricted contribution of the journal with an acknowledgement of publication in International Journal of MC Square Scientific Research.
- Authors are allowed and encouraged to share their work during the submission process for increasing citation and exploring to increase the paper availability in worldwide way. The Effect of Open Access.
