Feature Extraction Techniques for Iris Recognition System: A Survey
Keywords:
Biometrics, Computer vision, Feature extraction, Pattern recognition.Abstract
The extraction of features involves the method of converting the original pixel values of an image to more meaningful, useful and measurable information that can be used in other techniques, such as image processing, pattern recognition and machine learning. The feature extraction plays a predominant role in iris recognition in which also the recognition rate is determined. The effective recognition accuracy, reduction of misclassification of two iris templates mostly depends on feature extraction techniques. An efficient iris recognition system requires that the discriminating information presents in an iris pattern to be accurately obtained. This paper performed a literature review on different techniques of feature extraction of iris recognition. The recommendation was made on how these techniques can be further enhanced to produce an effective iris recognition system.
Downloads
References
Abhishree, T. M., Latha, J., Manikantan, K., & Ramachandran, S. (2015). Face Recognition using Gabor filter based Feature Extraction with Anisotropic Diffusion as a pre-processing technique. Procedia Computer Science, 45(C), 312–321. https://doi.org/10.1016/j.procs.2015.03.149
Abikoye, O. C., Aro, T. O., Ogundokun, O., & Akande, H. B. (2019). Comparative Analysis of Selected Feature Extraction Techniques for Iris Recognition System. FUW Trends in Science & Technology Journal, 3(2), 541–545.
Adegoke, B. O, Omidiora, E. O, Ojo, J. A., & Falohun, S. A. (2013). Iris feature extraction : A survey. Computer Engineering and Intelligent, 4(9), 7–14.
Arjunan, T. (2015). Iris Recognition using Invariant Moment Features Based on Haar Wavelet Decomposition. Indian Journal of Engineering, 12(30), 54–59. [5] Ashwini, M. B., Mohammad, I. &, & Fawaz, A. (2015). Evaluation of Iris Recognition System on Multiple Feature Extraction Algorithms and its Combinations. International Journal of Computer Applications Technology and Research, 4(8), 592–598. https://doi.org/10.7753/IJCATR0408.1002
Bansal, A., Agarwal, R., & Sharma, R. K. (2016). Statistical feature extraction based iris recognition system. [7] Sadhana, 41(5), 507–518.
https://doi.org/10.1007/s12046-016-0492-9
Biu., A. H., Husain, R., & Magaji, A. S. (2018). An Enhanced Iris Recognition and Authentication System Using Energy Measure. Science World Journal, 13(1), 11–17.
Chirchi, V. R. E., & Waghmare, L. M. (2013). Feature Extraction and Pupil Detection Algorithm Used for Iris Biometric Authentication System. 6(6), 141–160. https://doi.org/10.14257/ijsip.2013.6.6.14
Chouhan, B., & Shukla, S. (2010). Analysis of statistical feature extraction for Iris Recognition System using Laplacian of Gaussian filter. International Journal of Applied Engineering Research, Dindigul, 1(3), 528–535.
Devi, K., Gupta, P., Grover, D., & Dhindsa, A. (2016). An Effective Feature Extraction Approach for Iris Recognition System. Indian Journal of Science and Technology, 9(December), 1–5. https://doi.org/10.17485/ijst/2016/v9i47/106827
Fauna, K. K., & Athira, P. & R. K. J. S. (2016). A Review on Iris Feature Extraction Methods. International Journal of Engineering Research and General Science, 4(2), 663–667.
Gandhi, K. M., & Kulkarni, P. R. H. (2014). Sift Algorithm for Iris Feature Extraction. Global Journal of Computer Science and Technology Graphics & Vision, 14(3), 0–6.
Harsha, R., & Ramesha, K. (2015). DWT Based Feature Extraction for Iris Recognition. International Journal of Advanced Research in Computer and Communication Engineering, 4(5), 300–306. https://doi.org/10.17148/IJARCCE.2015.4567
He, Y., Feng, G., Hou, Y., Li, L., & Micheli-Tzanakou, E. (2011). Iris feature extraction method based on LBP and chunked encoding. Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011, 3, 1663–1667. https://doi.org/10.1109/ICNC.2011.6022302
Hira, Z. M., & Gillies, D. F. (2015). A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data. Advances in Bioinformatics, 198–363. https://doi.org/10.1155/2015/198363
Ibrahim, A. A. (2014). Iris Recognition using Haar Wavelet Transform. Journal of AL-Nahrain-University Science, 17(1), 80–86.
Imani, M. B., Pourhabibi, T., Keyvanpour, M. R., & Azmi, R. (2012). A New Feature Selection Method Based on Ant Colony and Genetic Algorithm on Persian Font Recognition. International Journal of Machine Learning and Computing, 2(3), 278–282.
Jain, A. K., Ross, A., & Pankanti, S. (2006). Biometrics : A Tool for Information Security. IEEE Transactions on Information Forensics and Security, 1(2), 125–143. https://doi.org/10.1109/TIFS.2006.873653
Joshua, T. P., Arrivukannamma, M., & Sathiaseelan, J. G. R. (2016). Comparison of DCT and DWT Image Compression. International Journal of Computer Science and Mobile Computer, 5(4), 3–8.
Jyoti, P., Parvati, B., & Kumar, R., & Agrawal, S. L. (2015). Performance Review of IRIS Recognition Systems. International Journal of Computer Systems, 02(12), 564–566.
Kapil, D., & Jain, E. A. (2015). A Brief Review on Feature-Based Approaches for Face Recognition. International Journal of Science and Research, 4(5), 273–277.
Kaur, M. & Jasjit, K. (2017). Review of Face Recognition Techniques. International Journal of Computer Applications, 164(6), 31–35.
Kaur, R., & Choudhary, P. (2016). A Review of Image Compression Techniques. International Journal of Computer Applications, 142(1), 8–11.
Kerim, A. A & Mohammed, S. J. (2014). New Iris Feature Extraction and Pattern Matching Based on Statistical Measurement. 3(5), 226–231.
Kumar, A., Potnis, A., & Singh, A. P. (2016). Iris recognition and feature extraction in iris recognition System by employing 2D DCT. International Research Journal of Engineering and Technology, 3(12), 503–510.
Miche, Y., Bas, P., Lendasse, A., Jutten, C., & Simula, O. (2007). Advantages of Using Feature Selection Techniques on Steganalysis Schemes. 9th International Work-Conference on Artificial Neural Networks, IWANN’2007, 606–613. https://doi.org/10.1007/978-3-540-73007-1_73
O’Connor, B., Roy, K., Shelton, J., & Dozier, G. (2014). Iris Recognition Using Fuzzy Level Set and GEFE. International Journal of Machine Learning and Computing, 4(3), 225–231. https://doi.org/10.7763/IJMLC.2014.V4.416
Oluwakemi, A., Sadiku, J. S., Kayode, A., & Rasheed, J. (2014). Iris Feature Extraction for Personal Identification using Fast Wavelet Transform (FWT). International Journal of Applied Information Systems, 6(9), 1–6.
Patel, K. S., Pithadiya, K. J., & Chauhan, J. C. (2015). International Journal of Advanced Engineering and Research 2D Palmprint Recognition : A Survey. International Journal of Advanced Engineering and Research Development, 2(7), 332–337.
Patil, R. B., & Deshmukh, R. R. (2013). A Review on Feature Extraction Techniques of Iris. International Journal of Engineering Research & Technology, 2(12), 2909–2912. [32] Prajwala, N. B., & Pushpa, N. B. (2019). Matching of Iris Pattern Using Image Processing. International Journal of Recent Technology and Engineering, 8(2), 21–23. https://doi.org/10.35940/ijrte.B1004.0982S1119
Rampally, D. (2010). Iris Recognition Based on Featured Extraction. Manhattan.
Ranjan, S. Prabu, S., Swarnalatha, P., Magesh, G., & Sundararajan, R. (2017). Iris Recognition System. International Research Journal of Engineering and Technology, 04(12), 864–868. https://doi.org/10.14569/ijacsa.2010.010106
Sarode, N. S., & Patil, A. M. (2015). Iris Recognition using LBP with Classifiers-KNN and NB. International Journal of Science and Research, 4(1), 1904–1908. [36] Shah, J. H., Sharif, M., Raza, M., & Azeem, A. (2013). A Survey : Linear and Nonlinear PCA Based Face Recognition Techniques. International Arab Journal of Information Technology, 10(6), 536–545.
Shi, J.-X., & Gu, X.-F. (2010). The comparison of iris recognition using principal component analysis, independent component analysis and Gabor wavelets. Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on, 1(1), 61–64. https://doi.org/10.1109/ICCSIT.2010.5563947
Shirke, S. D., & Gupta, D. (2013). Iris Recognition Using Gabor. International Journal of Computer Technology & Application, 4(1), 1–7.
Solanke, S. B., & Deshmukh, R. R. (2017). Enhanced Feature Extraction Technique for Iris Template Generation. International Journal of Computer Technology & Application, 8(August), 499–506.
Thiyaneswaran, B., & Padma, S. (2015). Analysis of Gabor Filter Parameter for Iris Feature Extraction. International Journal of Advanced Computer Technology, 3(5), 45–48.
Yu, L., & Liu, H. (2004). Efficient Feature Selection via Analysis of Relevance and Redundancy. Journal of Machine Learning Research, 5(1), 1205–1224. https://doi.org/10.1145/1014052.1014149
Zhao, Z., & Kumar, A. (2019). A deep learning based unified framework to detect, segment and recognize irises using spatially corresponding features. Pattern Recognition, 93, 546–557. https://doi.org/10.1016/j.patcog.2019.04.010
Zhen, X. (2013). Feature Extraction and Representation for Human Action Recognition. Retrieved from
http://www.intechopen.com/books/face-recognition/featur e-extraction-and-representation-for-recognition
