A New Feature Extraction Approach in Classification for Improving the Accuracy in Iris Recognition

Tara Qadir - Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia
Nik Taujuddin - Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia
Norfaiza Fuad - Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.7.4.1373

Abstract


Personal identity is becoming increasingly vital to meet the increasing security standards of today's business society. Iris recognition is one of the most accurate biometric technologies currently in use. Iris recognition is employed in high-security sectors due to its dependability and flawless identification rates. The steps of iris identification, comprising image preparation, extraction of features, and classifier creation, are described thoroughly in the primary portion of this research. The feature extraction stage is the most important in an iris identification system since it extracts the iris's distinctive feature. Several methods have been devised to extract the various characteristics that are unique to everyone. Modern iris identification systems frequently use Gabor filters to identify iris textural characteristics. However, in the application, it is necessary to identify the appropriate Gabor modules and to generate a pattern of iris Gabor characteristics. This research aims to provide a novel multi-channel Gabor filter and Wavelet filter for breaking down and extracting iris data from two different iris datasets. Because wavelet is the most scalable method of image processing, the research investigates using it to create a unique pattern for the iris recognition system. The MATLAB program is used to implement these ideas. CASIA and MMU are the datasets used for this purpose, and their comparative analysis is addressed in the research. To show how well the method performs, experimental results are given. We demonstrate through experiments that the suggested approach results in excellent iris identification performance.

Keywords


Biometric iris recognition; preprocessing; normalization; segmentation; feature extraction; wavelet; gabor filter

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References


B. S. Mahdi, M. J. Hadi, and A. R. Abbas, “Intelligent Security Model for Password Generation and Estimation Using Hand Gesture Features,” Big Data and Cognitive Computing, vol. 6, no. 4, p. 116, Oct. 2022, doi: 10.3390/bdcc6040116.

M. Hemamalini and R. Saranya, “Graphical password authentication using hybrid pin keypad,” Malaya Journal of Matematik, vol. S, no. 1, pp. 554–559, 2019, doi: 10.26637/mjm0s01/0100.

K. Leyfer and A. Spivak, “Continuous User Authentication by the Classification Method Based on the Dynamic Touchscreen Biometrics,” 2019.

M. L. Gavrilova et al., “Emerging Trends in Security System Design Using the Concept of Social Behavioural Biometrics,” Information Fusion for Cyber-Security Analytics, pp. 229–251, Oct. 2016, doi: 10.1007/978-3-319-44257-0_10.

K. Okokpujie et al., “Integration of Iris Biometrics in Automated Teller Machines for Enhanced User Authentication,” Lecture Notes in Electrical Engineering, pp. 219–228, Jul. 2018, doi: 10.1007/978-981-13-1056-0_23.

H. Hafeez, M. N. Zafar, C. A. Abbas, H. Elahi, and M. O. Ali, “Real-Time Human Authentication System Based on Iris Recognition,” Eng, vol. 3, no. 4, pp. 693–708, Dec. 2022, doi: 10.3390/eng3040047.

N. Othman, N. Houmani, and B. Dorizzi, “Quality-Based Super Resolution for Degraded Iris Recognition,” Pattern Recognition Applications and Methods, pp. 285–300, Nov. 2014, doi: 10.1007/978-3-319-12610-4_18.

M. A. Taha, H. M. Ahmed, and S. O. Husain, “Iris Features Extraction and Recognition based on the Scale Invariant Feature Transform (SIFT),” Webology, vol. 19, no. 1, pp. 171–184, Jan. 2022, doi: 10.14704/web/v19i1/web19013.

A. Poursaberi and B. N. Araabi, “Iris Recognition for Partially Occluded Images: Methodology and Sensitivity Analysis,” EURASIP Journal on Advances in Signal Processing, vol. 2007, no. 1, Sep. 2006, doi: 10.1155/2007/36751.

H. K. Rana, Md. S. Azam, Mst. R. Akhtar, J. M. W. Quinn, and M. A. Moni, “A fast iris recognition system through optimum feature extraction,” PeerJ Computer Science, vol. 5, p. e184, Apr. 2019, doi: 10.7717/peerj-cs.184.

E. H. Kaur and J. S. Sohal, “Iris localization using Hybrid Algorithm containing Circular Hough Transform, Fuzzy Clustering Method and Canny Edge Detector,” International Journal of Advanced Research in Computer Science, vol. 8, no. 3, [Online]. Available: www.ijarcs.info

S. S., R. T., and S. Shantharajah, “An optimized rubber sheet model for normalization phase of IRIS recognition,” Computer Science and Information Technologies, vol. 1, no. 3, pp. 126–134, Nov. 2020, doi:10.11591/csit.v1i3.p126-134.

J. G. Daugman, “High Confidence Visual Recognition of Persons by a Test of Statistical Independence,” 1993.

M. Abbasi, “Improving identification performance in iris recognition systems through combined feature extraction based on binary genetics,” SN Applied Sciences, vol. 1, no. 7, Jun. 2019, doi: 10.1007/s42452-019-0777-9.

Verma, Prateek, Maheedhar Dubey, Praveen Verma, and Somak Basu. "Daughman’s algorithm method for iris recognition—a biometric approach." International journal of emerging technology and advanced engineering 2, no. 6 (2012): 177-185.

M. Radouane, N. I. Zouggari, A. Amraoui, and M. Amraoui, “Fusion of Gabor filter and steerable pyramid to improve iris recognition system,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 11, no. 4, p. 1460, Dec. 2022, doi: 10.11591/ijai.v11.i4.pp1460-1468.

S. Joyce and S. Veni, “Iris Biometric Watermarking for Authentication Using Multiband Discrete Wavelet Transform and Singular-Value Decomposition 259 Original Scientific Paper.”

N. Kihal, S. Chitroub, A. Polette, I. Brunette, and J. Meunier, “Efficient multimodal ocular biometric system for person authentication based on iris texture and corneal shape,” IET Biometrics, vol. 6, no. 6, pp. 379–386, Feb. 2017, doi: 10.1049/iet-bmt.2016.0067.

K. Harini, Dr. G. Yamuna, and V. Santhiya, “Biometric Iris Recognition System using Multiscale Feature Extraction Method,” International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 6, pp. 2298–2303, Mar. 2020, doi:10.35940/ijrte.f8016.038620.

S. K. S. Modak and V. K. Jha, “A Novel Technique to Enhance Performance of Multibiometric Framework using Bin based Classifier Based on Multi-algorithm Score Level Fusion,” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 3, pp. 2156–2166, Jan. 2020, doi: 10.35940/ijitee.c8773.019320.

K. Wang and A. Kumar, “Toward More Accurate Iris Recognition Using Dilated Residual Features,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 12, pp. 3233–3245, Dec. 2019, doi: 10.1109/tifs.2019.2913234.

S. U. Khan, N. S. A. M. Taujuddin, T. O. Qadir, S. N. Khan, and Z. Khan, “Iris Recognition Through Feature Extraction Methods: A Biometric Approach,” 2021 IEEE 19th Student Conference on Research and Development (SCOReD), Nov. 2021, doi: 10.1109/scored53546.2021.9652775.

S. Naqeeb Khan, S. Urooj Khan, O. J. Nwobodo, and K. A. Cyran, “Iris Recognition Through Edge Detection Methods: Application in Flight Simulator User Identification.” [Online]. Available: www.ijacsa.thesai.org

A. K. Bhateja, S. Sharma, S. Chaudhury, and N. Agrawal, “Iris recognition based on sparse representation and k-nearest subspace with genetic algorithm,” Pattern Recognition Letters, vol. 73, pp. 13–18, Apr. 2016, doi: 10.1016/j.patrec.2015.12.009.

I. Naseem, A. Aleem, R. Togneri, and M. Bennamoun, “Iris recognition using class-specific dictionaries,” Computers & Electrical Engineering, vol. 62, pp. 178–193, Aug. 2017, doi: 10.1016/j.compeleceng.2015.12.017.

R. Subban, N. Susitha, and D. P. Mankame, “Efficient iris recognition using Haralick features based extraction and fuzzy particle swarm optimization,” Cluster Computing, vol. 21, no. 1, pp. 79–90, May 2017, doi: 10.1007/s10586-017-0934-0.