Iris Recognition in Cross-Spectral Based on Histogram of Oriented Gradient and Linear Discriminant Analysis
DOI:
https://doi.org/10.29304/jqcm.2023.15.3.1267Keywords:
Iris Recognition, Cross-spectral matching, HOG, LDA, KNN, SVMAbstract
The use of biological and behavioural characteristics has significantly increased in recent years. Like fingerprints, faces, iris, and others in numerous crucial applications in security and electronic governance. Where recently, Research has concentrated on illuminating the recognition of iris images obtained in different fields across spectra by sensors that can capture double iris images in various areas (environment/lighting) by infrared Nearby and visible illumination (NIR, VIS) due to features unique in the iris texture that differ for the same individual, between their left and right eyes. But there is challenging to match NIR and VIS photographs due to the spectrum differences between pictures captured by near-infrared and visible light. We suggest utilizing the Histogram of Oriented Gradient (HOG) approach to extract meaningful information from images of the iris and implement Normalization to reduce differences in illumination, also apply the LDA for dimensionality reduction and K-Nearest Neighbor and Support Vector Machine as a classifier. The experimental findings were implemented on the PolyU bi-spectral dataset, and the results show that the suggested approach achieved exceptional matching performance by KNN, SVM on (Right to Left) for each (NIR, VIS) or versa (99.14%,99.04%, 97.44%,96.38%) respectively.
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References
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[26] The Hong Kong Polytechnic University Cross-Spectral Iris Images Database, Aug.2018 [online] Available: http://www4.comp.polyu.edu.hk.
[2] A. Adekunle et al., “Feature Extraction Techniques for Iris Recognition System: A Survey,” Int. J. Innov. Res. Comput. Sci. Technol., vol. 8, no. 2, pp. 37–42, 2020, doi: 10.21276/ijircst.2020.8.2.5.
[3] S. Adamović, V. Miškovic, N. Maček, and M. Milosavljević, “An efficient novel approach for iris recognition based on stylometric features and machine learning techniques,” Futur. Gener. Comput. Syst., vol. 107, pp. 144–157, 2020, doi: 10.1016/j.future.2020.01.056.
[4] P. Patil and K. Vasanth, "Iris Recognition Using Local and Global Iris Image Moment Features," 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India, 2019, pp. 1-5, doi: 10.1109/i-PACT44901.2019.8960219
[5] P. R. Nalla and A. Kumar, "Toward More Accurate Iris Recognition Using Cross-Spectral Matching," in IEEE Transactions on Image Processing, vol. 26, no. 1, pp. 208-221, Jan. 2017, doi: 10.1109/TIP.2016.2616281.
[6] K. Wang and A. Kumar, "Cross-Spectral Iris Recognition using CNN and Supervised Discrete Hashing," Pattern Recognition vol. 86 no. August, pp. 1–39, 2018, doi.org/10.1016/j.patcog.2018.08.010.
[7] M. Oktiana, T. Horiuchi, K. Hirai, K. Saddami, and F. Arnia, "Heliyon Cross-spectral iris recognition using phase-based matching and homomorphic filtering," Heliyon, vol. 6, no. April 2019, p. e03407, 2020, doi: 10.1016/j.heliyon.2020.e03407
[8] M. Mostofa, F. Taherkhani, J. Dawson and N. M. Nasrabadi, "Cross-Spectral Iris Matching Using Conditional Coupled GAN," 2020 IEEE International Joint Conference on Biometrics (IJCB), Houston, TX, USA, 2020, pp. 1-9, doi: 10.1109/IJCB48548.2020.9304929
[9] Vyas, R., Kanumuri, T. & Sheoran, G." Cross spectral iris recognition for surveillance-based applications." Multimed Tools Appl 78, 5681–5699 (2019),doi. org/10.1007/s11042-018-5689-y
[10] M. Oktiana et al., "Advances in Cross-Spectral Iris Recognition Using Integrated Gradientface-Based Normalization," in IEEE Access, vol. 7, pp. 130484-130494, 2019, doi 10.1109/ACCESS.2019.2939326.
[11] M. Oktiana, K. Saddami, F. Arnia, Y. Away, and K. Munadi, "Improved Cross-Spectral Iris Matching Using Multi-Scale Weberface and Gabor Local Binary Pattern," 2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Iasi, Romania, 2018, pp. 1-6, doi: 10.1109/ECAI.2018.8679097
[12] Vishwakarma, V. P., & Dalal, S. (2020). A novel approach for compensation of light variation effects with KELM classification for efficient face recognition. In Advances in VLSI, Communication, and Signal Processing: Select Proceedings of VCAS 2018 (pp. 1003-1012). Springer Singapore.
[13] Dalal, S., & Vishwakarma, V. P. (2020). A novel approach of face recognition using optimized adaptive illumination–Normalization and KELM. Arabian Journal for Science and Engineering, 45, 9977-9996.
[14] Chandrakala, M., & Devi, P. D. (2021). Two-stage classifier for face recognition using HOG features. Materials Today: Proceedings, 47, 5771-5775.
[15] Ghaffari, S., Soleimani, P., Li, K. F., & Capson, D. W. (2020). Analysis and comparison of FPGA-based histogram of oriented gradients implementations. IEEE Access, 8, 79920-79934.
[16] Mostafiz, R., Uddin, M. S., Alam, N. A., Hasan, M. M., & Rahman, M. M. (2021). MRI-based brain tumour detection using the fusion of histogram oriented gradients and neural features. Evolutionary Intelligence, 14, 1075-1087.
[17] Anowar, F., Sadaoui, S., & Selim, B. (2021). Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne). Computer Science Review, 40, 100378
[18] Choubey, D. K., Kumar, M., Shukla, V., Tripathi, S., & Dhandhania, V. K. (2020). Comparative analysis of classification methods with PCA and LDA for diabetes. Current diabetes reviews, 16(8), 833-850.
[19] Fabiyi, S. D., Murray, P., Zabalza, J., & Ren, J. (2021). Folded LDA: extending the linear discriminant analysis algorithm for feature extraction and data reduction in hyperspectral remote sensing. IEEE Journal of selected topics in applied earth observations and remote sensing, 14, 12312-12331.
[20]H. S. Hu and D. Z. Feng, “Minimum eigenvector collaborative representation discriminant projection for feature extraction,” Sensors (Switzerland), vol. 20, no. 17, pp. 1–20, 2020, doi: 10.3390/s20174778.
[21]J. Vasavi and M. Abirami, “A Qualitative Performance Comparison Of Supervised Machine Learning Algorithms For Iris Recognition,” Eur. J. Mol. Clin. Med., vol. 07, no. 06, p. 2020, 2020.
[22] Berry, M. W., Mohamed, A., & Yap, B. W. (Eds.). (2019). Supervised and unsupervised learning for data science. Springer Nature.
[23] Saranya, T., Sridevi, S., Deisy, C., Chung, T. D., & Khan, M. A. (2020). Performance analysis of machine learning algorithms in intrusion detection system: A review. Procedia Computer Science, 171, 1251-1260.
[24] Dey, S., Wasif, S., Tonmoy, D. S., Sultana, S., Sarkar, J., & Dey, M. (2020, February). A comparative study of support vector machine and Naive Bayes classifier for sentiment analysis on Amazon product reviews. In 2020 International Conference on Contemporary Computing and Applications (IC3A) (pp. 217-220). IEEE.
[25] Parady, G., Ory, D., & Walker, J. (2021). The overreliance on statistical goodness-of-fit and under-reliance on model validation in discrete choice models: A review of validation practices in the transportation academic literature. Journal of Choice Modelling, 38, 100257.
[26] The Hong Kong Polytechnic University Cross-Spectral Iris Images Database, Aug.2018 [online] Available: http://www4.comp.polyu.edu.hk.
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Published
2023-09-30
How to Cite
AL-kardhi, N. M., & Al-juboori, A. M. (2023). Iris Recognition in Cross-Spectral Based on Histogram of Oriented Gradient and Linear Discriminant Analysis. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(3), Comp Page 83–97. https://doi.org/10.29304/jqcm.2023.15.3.1267
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Computer Articles
