Contact Lens Detection Using Domain Specific BSIF and Discrete Wavelet Transform

Muhamad Ilham Aji Vachroni(1*), Raden Sumiharto(2), Dyah Aruming Tyas(3),

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(*) Corresponding Author
DOI: https://doi.org/10.23917/khif.v9i2.20084

Abstract

Iris is one of the reliable biometrics because it has a texture that rich properties and the texture is not changeable lifetime. Iris recognition has drawbacks in the matching process when using contact lenses. Contact lens can changes in the texture of the iris, which can reduce the accuracy of recognition. Therefore, a system is needed to detect contact lenses while someone is detected using contact lens, the system can reject the registration or authentication process. Methods used to detect contact lenses are Domain Specific Binarized Statistical Image Feature (BSIF) and Discrete Wavelet Transform (DWT) for feature extraction. Both methods are fused and modeled using the Support Vector Machine (SVM). Based on the test results, the most optimal kernel is 5x5 12bit. Using the kernel, the accuracy and f1 score obtained 99.1%. In the experiments conducted, this research applies Principal Component Analysis (PCA) to reduce features. However, the role of PCA does not affect the performance of the model. The best model tested with real life data, the Pocophone f1 smartphone and CCTV were used to take pictures of the eyes. The Result 6 experiments wich are 4 without contact lenses and 2 wearing contact lenses, there are only 2 detected correctly. This is because the ability of the images taken from the Poco F1 and CCTV have low resolution.

Keywords

contact lens detection;Domain Specific BSIF;Discrete Wavelet Transform;SVM;Computer Vision;Hancrafted;Iris

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References

D. Gragnaniello, G. Poggi, C. Sansone, and L. Verdoliva, “Using iris and sclera for detection and classification of contact lenses,” Pattern Recognit. Lett., vol. 82, pp. 251

, Oct. 2016, doi: 10.1016/j.patrec.2015.10.009.

G. Singh, R. K. Singh, R. Saha, and N. Agarwal, “IWT Based Iris Recognition for Image Authentication,” Procedia Comput. Sci., vol. 171, no. 2019, pp. 1868–1876, 2020, doi: 10.1016/j.procs.2020.04.200.

H. Y. Sun Yangqing, Image Preprocessing of Iris Recognition. IEEE, 2018.

D. Yadav, N. Kohli, J. S. Doyle, R. Singh, M. Vatsa, and K. W. Bowyer, “Unraveling the Effect of Textured Contact Lenses on Iris Recognition,” 2013. [Online]. Available: http://www3.nd.edu/.

A. Nigam, B. Kumar, and P. Gupta, “Robust Contact Lens Detection Using Local Phase Quantization and Binary Gabor Pattern,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9256, pp. 702–714, 2015, doi: 10.1007/978-3-319-23192-1.

M. R. Dronky, W. Khalifa, and M. Roushdy, “Using residual images with BSIF for iris liveness detection,” Expert Syst. Appl., vol. 182, Nov. 2021, doi: 10.1016/j.eswa.2021.115266.

M. R. Dronky, W. Khalifa, and M. Roushdy, “Impact of segmentation on iris liveness detection,” 2019.

J. S. Doyle and K. W. Bowyer, “Robust Detection of Textured Contact Lenses in Iris Recognition Using BSIF,” IEEE Access, vol. 3, pp. 1672–1683, 2015, doi: 10.1109/ACCESS.2015.2477470.

A. Czajka, D. Moreira, K. W. Bowyer, and P. J. Flynn, “Domain-specific human-inspired binarized statistical image features for Iris recognition,” Proc. - 2019 IEEE Winter Conf. Appl. Comput. Vision, WACV 2019, pp. 959–967, 2019, doi: 10.1109/WACV.2019.00107.

S. Dhanya and V. S. Kumari Roshni, “Comparison of various texture classification methods using multiresolution analysis and linear regression modeling,” Springerplus, vol. 5, no. 1, pp. 1–18, 2016, doi: 10.1186/s40064-015-1631-1.

K. Gopala Krishnan and P. T. Vanathi, “An efficient texture classification algorithm using integrated Discrete Wavelet Transform and local binary pattern features,” Cogn. Syst. Res., vol. 52, pp. 267–274, 2018, doi: 10.1016/j.cogsys.2018.07.015.

T. Parhizkar, E. Rafieipour, and A. Parhizkar, “Evaluation and improvement of energy consumption prediction models using principal component analysis based feature reduction,” J. Clean. Prod., vol. 279, 2021, doi: 10.1016/j.jclepro.2020.123866.

N. Kohli, D. Yadav, M. Vatsa, and R. Singh, “Revisiting iris recognition with color cosmetic contact lenses,” Proc. - 2013 Int. Conf. Biometrics, ICB 2013, vol. 1, 2013, doi: 10.1109/ICB.2013.6613021.

H. A. Biu, R. Husain, and A. S. Magaji, “AN ENHANCED IRIS RECOGNITION AND AUTHENTICATION SYSTEM USING ENERGY MEASURE,” Sci. World J., vol. 13, no. 1, 2018, [Online]. Available: www.scienceworldjournal.org.

H. K. Rana, M. S. Azam, M. R. Akhtar, J. M. W. Quinn, and M. A. Moni, “A fast iris recognition system through optimum feature extraction,” PeerJ Comput. Sci., vol. 2019, no. 4, pp. 1–13, 2019, doi: 10.7717/peerj-cs.184.

J. Daugman, “How Iris Recognition Works,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 21–30, Jan. 2004, doi: 10.1109/TCSVT.2003.818350.

B. Attallah, A. Serir, Y. Chahir, and A. Boudjelal, “Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction,” J. Electron. Imaging, vol. 26, no. 06, p. 1, 2017, doi: 10.1117/1.jei.26.6.063006.

I. T. Jollife and J. Cadima, “Principal component analysis: A review and recent developments,” Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., vol. 374, no. 2065, 2016, doi: 10.1098/rsta.2015.0202.

H. Hasan, H. Z. M. Shafri, and M. Habshi, “A Comparison between Support Vector Machine (SVM) and Convolutional Neural Network (CNN) Models for Hyperspectral Image Classification,” IOP Conf. Ser. Earth Environ. Sci., vol. 357, no. 1, 2019, doi: 10.1088/1755-1315/357/1/012035

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