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Qadir, Tara Othman
- Feature Extraction Methods For Iris Recognition System: A Survey
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Authors
Affiliations
1 Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), 86400, Parit Raja, Johor, MY
2 Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Johor, MY
1 Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), 86400, Parit Raja, Johor, MY
2 Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Johor, MY
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 14, No 1 (2022), Pagination: 99-110Abstract
Protection has become one of the biggest fields of study for several years, however the demand for this is growing exponentially mostly with rise in sensitive data. The quality of the research can differ slightly from any workstation to cloud, and though protection must be incredibly important all over. Throughout the past two decades, sufficient focus has been given to substantiation along with validation in the technology model. Identifying a legal person is increasingly become the difficult activity with the progression of time. Some attempts are introduced in that same respect, in particular by utilizing human movements such as fingerprints, facial recognition, palm scanning, retinal identification, DNA checking, breathing, speech checker, and so on. A number of methods for effective iris detection have indeed been suggested and researched. A general overview of current and state-of-the-art approaches to iris recognition is presented in this paper. In addition, significant advances in techniques, algorithms, qualified classifiers, datasets and methodologies for the extraction of features are also discussed.Keywords
Bio-metric traits, iris patterns, feature extraction, SVM, wavelet transform, iris security.Full Text
References
- Bowyer, K. W., & Burge, M. J. (Eds.). (2016). Handbook of iris recognition. Springer London.
- Hasan, O., & Tahar, S. (2015). Formal verification methods. In Encyclopedia of Information Science and Technology, Third Edition (pp. 7162-7170). IGI Global.
- Harz, D., & Knottenbelt, W. (2018). Towards safer smart contracts: A survey of languages and verification methods. arXiv preprint arXiv:1809.09805.
- Rajhans, A., & Krogh, B. H. (2012, April). Heterogeneous verification of cyber-physical systems using behavior relations. In Proceedings of the 15th ACM international conference on Hybrid Systems: Computation and Control (pp. 35-44).
- He, Z., Sun, Z., Tan, T., Qiu, X., Zhong, C., & Dong, W. (2008, June). Boosting ordinal features for accurate and fast iris recognition. In 2008 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). IEEE.
- He, Z., Tan, T., Sun, Z., & Qiu, X. (2008, October). Robust eyelid, eyelash and shadow localization for iris recognition. In 2008 15th IEEE International Conference on Image Processing (pp. 265-268). IEEE.
- Prabhakar, S., Pankanti, S., & Jain, A. K. (2003). Biometric recognition: Security and privacy concerns. IEEE security & privacy, 99(2), 33-42.
- Jain, A. K., Ross, A., & Prabhakar, S. (2004). An introduction to biometric recognition. IEEE Transactions on circuits and systems for video technology, 14(1), 4-20.
- Thepade, S. D., & Bidwai, P. (2013, August). Iris recognition using fractional coefficients of transforms, Wavelet Transforms and Hybrid Wavelet Transforms. In Control Computing Communication & Materials (ICCCCM), 2013 International Conference on (pp. 1-5). IEEE.
- Dhage, S. S., Hegde, S. S., Manikantan, K., & Ramachandran, S. (2015). DWT-based feature extraction and radon transform based contrast enhancement for improved iris recognition. Procedia Computer Science, 45, 256-265.
- Kekre, H. B., Thepade, S. D., Jain, J., & Agrawal, N. (2011, February). Iris recognition using texture features extracted from walshlet pyramid. In Proceedings of the International Conference & Workshop on Emerging Trends in Technology (pp. 76-81). ACM.
- Suciati, N., Anugrah, A. B., Fatichah, C., Tjandrasa, H., Arifin, A. Z., Purwitasari, D., & Navastara, D. A. (2016, October). Feature extraction using statistical moments of wavelet transform for iris recognition. In Information & Communication Technology and Systems (ICTS), 2016 International Conference on (pp. 193-198). IEEE.
- Sharma, V. P., Mishra, S. K., & Dubey, D. (2013, September). Improved Iris Recognition System Using Wavelet Transform and Ant Colony Optimization. In Computational Intelligence and Communication Networks (CICN), 2013 5th International Conference on (pp. 243-246). IEEE.
- Jadhav, T. H., & Dewan, J. H. (2016). Iris Recognition using Self Mutated Hybrid Wavelet Transform using Cosine Haar Hartley and Slant Transforms with Partial Energies of Transformed Iris Images. International Journal of Computer Applications (IJCA), 140, 0975-8887.
- Minaee, S., Abdolrashidi, A., & Wang, Y. (2015, August). Iris recognition using scattering transform and textural features. In Signal Processing and Signal Processing Education Workshop (SP/SPE), 2015 IEEE (pp. 37-42). IEEE.
- Raja, K. B., Raghavendra, R., Vemuri, V. K., & Busch, C. (2015). Smartphone based visible iris recognition using deep sparse filtering. Pattern Recognition Letters, 57, 33-42.
- Alvarez-Betancourt, Y., & Garcia-Silvente, M. (2016). A keypoints-based feature extraction method for iris recognition under variable image quality conditions. Knowledge-Based Systems, 92, 169-182.
- Duraipandi, C., Pratap, A., & Uthariaraj, R. (2014, April). A grid based iris biometric watermarking using wavelet transform. In Recent Trends in Information Technology (ICRTIT), 2014 International Conference on (pp. 1-6). IEEE.
- Tajouri, I., Ghorbel, A., Aydi, W., & Masmoudi, N. (2016, December). An efficient iris texture analysis based on HAAR wavelet 2D Log Gabor and monogenic filter. In Sciences and Techniques of Automatic Control and Computer Engineering (STA), 2016 17th International Conference on (pp. 153-157). IEEE.
- Rahulkar, A. D., & Holambe, R. S. (2012). Partial iris feature extraction and recognition based on a new combined directional and rotated directional wavelet filter banks. Neurocomputing, 81, 12-23.
- Bhateja, A. K., Sharma, S., Chaudhury, S., & Agrawal, N. (2016). Iris recognition based on sparse representation and k-nearest subspace with genetic algorithm. Pattern Recognition Letters, 73, 13-18.
- Bowyer, K. W., Hollingsworth, K., & Flynn, P. J. (2008). Image understanding for iris biometrics: A survey. Computer vision and image understanding, 110(2), 281-307.
- He, Z., Tan, T., & Sun, Z. (2006, August). Iris localization via pulling and pushing. In Pattern Recognition, 2006. ICPR 2006. 18th International Conference on (Vol. 4, pp. 366-369). IEEE.
- De Mira, J., & Mayer, J. (2003, October). Image feature extraction for application of biometric identification of iris-a morphological approach. In Computer Graphics and Image Processing, 2003. SIBGRAPI 2003. XVI Brazilian Symposium on (pp. 391-398). IEEE.
- Guo, G., & Jones, M. J. (2008, January). Iris extraction based on intensity gradient and texture difference. In Applications of Computer Vision, 2008. WACV 2008. IEEE Workshop on (pp. 1-6). IEEE.
- Ma, L., Tan, T., Wang, Y., & Zhang, D. (2003). Personal identification based on iris texture analysis. IEEE transactions on pattern analysis and machine intelligence, 25(12), 1519-1533.
- Daugman, J. (2009). How iris recognition works. In The essential guide to image processing (pp. 715-739).
- Daugman, J. G. (1993). High confidence visual recognition of persons by a test of statistical independence. IEEE transactions on pattern analysis and machine intelligence, 15(11), 1148-1161.
- de Martin-Roche, D., Sanchez-Avila, C., & Sanchez-Reillo, R. (2001, October). Iris recognition for biometric identification using dyadic wavelet transform zero-crossing. In Security Technology, 2001 IEEE 35th International Carnahan Conference on (pp. 272-277). IEEE.
- Masek, L. (2003). Recognition of human iris patterns for biometric identification.
- Liu, X., Bowyer, K. W., & Flynn, P. J. (2005, June). Experimental evaluation of iris recognition. In Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on (pp. 158-158). IEEE.
- Hollingsworth, K., Baker, S., Ring, S., Bowyer, K. W., & Flynn, P. J. (2009, May). Recent research results in iris biometrics. In Optics and Photonics in Global Homeland Security V and Biometric Technology for Human Identification VI (Vol. 7306, p. 73061Y). International Society for Optics and Photonics.
- Wildes, R. P., Asmuth, J. C., Green, G. L., Hsu, S. C., Kolczynski, R. J., Matey, J. R., & McBride, S. E. (1996). A machine-vision system for iris recognition. Machine vision and Applications, 9(1), 1-8.
- Wildes, R. P. (1997). Iris recognition: an emerging biometric technology. Proceedings of the IEEE, 85(9), 1348-1363.
- Wildes, R. P., Asmuth, J. C., Green, G. L., Hsu, S. C., Kolczynski, R. J., Matey, J. R., & McBride, S. E. (1994, December). A system for automated iris recognition. In Applications of Computer Vision, 1994., Proceedings of the Second IEEE Workshop on (pp. 121-128). IEEE.
- Krichen, E., Mellakh, M. A., Garcia-Salicetti, S., & Dorizzi, B. (2004, August). Iris identification using wavelet packets. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on (Vol. 4, pp. 335-338). IEEE.
- Boles, W. W., & Boashash, B. (1998). A human identification technique using images of the iris and wavelet transform. IEEE transactions on signal processing, 46(4), 1185-1188.
- Ma, L., Tan, T., Wang, Y., & Zhang, D. (2004). Efficient iris recognition by characterizing key local variations. IEEE Transactions on Image processing, 13(6), 739-750.
- Ko, J. G., Gil, Y. H., Yoo, J. H., & Chung, K. I. (2010). U.S. Patent No. 7,715,594. Washington, DC: U.S. Patent and Trademark Office.
- Tajbakhsh, N., Misaghian, K., & Bandari, N. M. (2009, September). A region-based iris feature
- extraction method based on 2D-wavelet transform. In European Workshop on Biometrics and Identity Management (pp. 301-307). Springer, Berlin, Heidelberg.
- Huang, Y. P., Luo, S. W., & Chen, E. Y. (2002). An efficient iris recognition system. In Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on (Vol. 1, pp. 450-454). IEEE.
- Radhika, K. R., Sheela, S. V., Venkatesha, M. K., & Sekhar, G. N. (2009, September). Multi-modal authentication using continuous dynamic programming. In European Workshop on Biometrics and Identity Management (pp. 228-235). Springer, Berlin, Heidelberg.
- Al-Waisy, A. S., Qahwaji, R., Ipson, S., Al-Fahdawi, S., & Nagem, T. A. (2018). A multi-biometric iris recognition system based on a deep learning approach. Pattern Analysis and Applications, 21(3), 783-802.
- Dua, M., Gupta, R., Khari, M., & Crespo, R. G. (2019). Biometric iris recognition using radial basis function neural network. Soft Computing, 23(22), 11801-11815.
- Ahmadi, N., Nilashi, M., Samad, S., Rashid, T. A., & Ahmadi, H. (2019). An intelligent method for iris recognition using supervised machine learning techniques. Optics & Laser Technology, 120, 105701.