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Compression-Robust and Fuzzy-Based Feature-Fusion Model for Optimizing the Iris Recognition

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08/09/2020 | Issue 1/2021

Magazine:
Wireless Personal Communications> Issue 1/2021
Authors:
Kapil Juneja, Chhavi Rana

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Abstract

Iris Recognition is gaining popularity in various online and offline authentication and multi-model biometric systems. The non-altering and non-obscuring nature of Iris have increased its reliability in authentication systems. The iris images captured in an uncontrolled environment and situation is the challenging issue of the iris recognition. In this paper, a compression robust and KPCA-Gabor fused model is presented to recognize the iris image accurately under these complexities. The illumination and noise robustness is included in this pre-processing stage for gaining the robustness and reliability against complex capturing. The effective compression features are generated as a phase pre-treatment vector using the logarithmic quantization method. (Kernel Principal Component Analysis) KPCA and Gabor filters are applied to the rectified image for generating the textural features. The compression is also applied to Gabor and KPCA filtered images. The fuzzy adaptive content level fusion is applied to the compression image, KPCA compression, and Gabor compression iris image. (K-Nearest Neighbors) KNN based mapping is used to this composite-fused and reduced feature set to recognize the individual. The proposed compression and fusion-feature based model is applied to CASIA-Iris, UBIRIS, and IITD datasets. The comparative evaluations against earlier approaches identify that the proposed model has improved the recognition accuracy and the reduction in error-rate is also achieved.

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literature
Go back to reference Juneja, K., & Gill, N. S. (2015). A PCT / PST improved HMM-PCA model for pose robust facial recognition. In International conference on applied and theoretical computing and communication technology, Davangere, 2015 (pp 131-136). Juneja, K., & Gill, N. S. (2015). A PCT / PST improved HMM-PCA model for pose robust facial recognition. In International conference on applied and theoretical computing and communication technology, Davangere, 2015 (pp 131-136).
Go back to reference Nguyen, K., Fookes, C., Jillela, R., Sridharan, S., & Ross, A. (2017). Long range iris recognition: A survey. Pattern Recognition, 72, 123-143. CrossRef Nguyen, K., Fookes, C., Jillela, R., Sridharan, S., & Ross, A. (2017). Long range iris recognition: A survey. Pattern Recognition, 72, 123-143. CrossRef
Go back to reference De Marsico, M., Petrosino, A., & Ricciardi, S. (2016). Iris recognition through machine learning techniques: A survey. Pattern Recognition Letters, 82, 106-115. CrossRef De Marsico, M., Petrosino, A., & Ricciardi, S. (2016). Iris recognition through machine learning techniques: A survey. Pattern Recognition Letters, 82, 106-115. CrossRef
Go back to reference Daugman, J., & Downing, C. (2008). Effect of severe image compression on iris recognition performance. IEEE Transactions on Information Forensics and Security, 3 (1), 52-61. CrossRef Daugman, J., & Downing, C. (2008). Effect of severe image compression on iris recognition performance. IEEE Transactions on Information Forensics and Security, 3 (1), 52-61. CrossRef
Go back to reference Hassan, R., Kasim, S., Esa, N. M., & Zakaria, Z. (2017). Comparative study of different window sizes setting in median filter for off-angle iris recognition. International Journal on Advanced Science Engineering and Information Technology, 7, 1638-1643. CrossRef Hassan, R., Kasim, S., Esa, N. M., & Zakaria, Z. (2017). Comparative study of different window sizes setting in median filter for off-angle iris recognition. International Journal on Advanced Science Engineering and Information Technology, 7, 1638-1643. CrossRef
Go back to reference Hofbauer, H., Alonso-Fernandez, F., Bigun, J., & Uhl, A. (2016). Experimental analysis regarding the influence of iris segmentation on the recognition rate. IET Biometrics, 5 (3), 200-211. CrossRef Hofbauer, H., Alonso-Fernandez, F., Bigun, J., & Uhl, A. (2016). Experimental analysis regarding the influence of iris segmentation on the recognition rate. IET Biometrics, 5 (3), 200-211. CrossRef
Go back to reference Chen, J., Shen, F., Chen, D. Z., & Flynn, P. J. (2016). Iris recognition based on human-interpretable features. IEEE Transactions on Information Forensics and Security, 11 (7), 1476-1485. CrossRef Chen, J., Shen, F., Chen, D. Z., & Flynn, P. J. (2016). Iris recognition based on human-interpretable features. IEEE Transactions on Information Forensics and Security, 11 (7), 1476-1485. CrossRef
Go back to reference Bergmüller, T., Christopoulos, E., Fehrenbach, K., Schnöll, M., & Uhl, A. (2017). Recompression effects in iris recognition. Image and Vision Computing, 58, 142-157. CrossRef Bergmüller, T., Christopoulos, E., Fehrenbach, K., Schnöll, M., & Uhl, A. (2017). Recompression effects in iris recognition. Image and Vision Computing, 58, 142-157. CrossRef
Go back to reference Kaur, B., Singh, S., & Kumar, J. (2018). Iris recognition using zernike moments and polar harmonic transforms. Arabian Journal for Science and Engineering, 43 (12), 7209-7218. CrossRef Kaur, B., Singh, S., & Kumar, J. (2018). Iris recognition using zernike moments and polar harmonic transforms. Arabian Journal for Science and Engineering, 43 (12), 7209-7218. CrossRef
Go back to reference Sahu, B., Sa, P. K., Bakshi, S., & Sangaiah, A. K. (2018). Reducing dense local feature key-points for faster iris recognition. Computers & Electrical Engineering, 70, 939-949. CrossRef Sahu, B., Sa, P. K., Bakshi, S., & Sangaiah, A. K. (2018). Reducing dense local feature key points for faster iris recognition. Computers & Electrical Engineering, 70, 939-949. CrossRef
Go back to reference Kaur, B., Singh, S., & Kumar, J. (2018). Robust iris recognition using moment invariants. Wireless Personal Communications, 99 (2), 799-828. CrossRef Kaur, B., Singh, S., & Kumar, J. (2018). Robust iris recognition using moment invariants. Wireless Personal Communications, 99 (2), 799-828. CrossRef
Go back to reference Gupta, R., & Gupta, K. (2014). Iris recognition using templates fusion with weighted majority voting. International Journal of Image and Data Fusion, 2014, 1-16. Gupta, R., & Gupta, K. (2014). Iris recognition using templates fusion with weighted majority voting. International Journal of Image and Data Fusion, 2014, 1-16.
Go back to reference Naseem, I., Aleem, A., Togneri, R., & Bennamoun, M. (2017). Iris recognition using class-specific dictionaries. Computers & Electrical Engineering, 62, 178-193. CrossRef Naseem, I., Aleem, A., Togneri, R., & Bennamoun, M. (2017). Iris recognition using class-specific dictionaries. Computers & Electrical Engineering, 62, 178-193. CrossRef
Go back to reference Suvarchala, P. V. L., & Srinivas Kumar, S. (2018). Texture synthesis and modified filter bank in contourlets for improved iris recognition. Pattern Analysis and Applications, 21 (4), 1127-1138. MathSciNetCrossRef Suvarchala, P. V. L., & Srinivas Kumar, S. (2018). Texture synthesis and modified filter bank in contourlets for improved iris recognition. Pattern Analysis and Applications, 21 (4), 1127-1138. MathSciNetCrossRef
Go back to reference 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. CrossRef 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. CrossRef
Go back to reference Umer, S., Dhara, B. C., & Chanda, B. (2015). Iris recognition using multi-scale morphologic features. Pattern Recognition Letters, 65, 67-74. CrossRef Umer, S., Dhara, B. C., & Chanda, B. (2015). Iris recognition using multi-scale morphologic features. Pattern Recognition Letters, 65, 67-74. CrossRef
Go back to reference Tan, C.-W., & Kumar, A. (2014). Efficient and accurate at-a-distance iris recognition using geometric key-based iris encoding. IEEE Transactions on Information Forensics and Security, 9 (9), 1518-1526. CrossRef Tan, C.-W., & Kumar, A. (2014). Efficient and accurate at-a-distance iris recognition using geometric key-based iris encoding. IEEE Transactions on Information Forensics and Security, 9 (9), 1518-1526. CrossRef
Go back to reference Ali, L. E., Luo, J., & Ma, J. (2016). Iris recognition from distant images based on multiple feature descriptors and classifiers. In 13th international conference on signal processing (ICSP), Chengdu, 2016 (pp. 1357-1362). Ali, L. E., Luo, J., & Ma, J. (2016). Iris recognition from distant images based on multiple feature descriptors and classifiers. In 13th international conference on signal processing (ICSP), Chengdu, 2016 (pp. 1357-1362).
Go back to reference Juneja, K., & Gill, N. S. (2015). Tied multi-rubber band model for camera distance, shape and head movement robust facial recognition. In International conference on applied and theoretical computing and communication technology, 2015 (pp. 218–223). Juneja, K., & Gill, N. S. (2015). Tied multi-rubber band model for camera distance, shape and head movement robust facial recognition. In International conference on applied and theoretical computing and communication technology, 2015 (pp. 218–223).
Go back to reference Hamouchene, I., & Aouat, S. (2016). Efficient approach for iris recognition. Signal, Image and Video Processing, 10 (7), 1361-1367. CrossRef Hamouchene, I., & Aouat, S. (2016). Efficient approach for iris recognition. Signal, Image and Video Processing, 10 (7), 1361-1367. CrossRef
Go back to reference Kaewphaluk, K., & Widjaja, J. (2017). Experimental demonstrations of noise-robustness of compression-based joint wavelet transform correlator in retinal recognition. Optics - International Journal for Light and Electron Optics, 142, 168-173. CrossRef Kaewphaluk, K., & Widjaja, J. (2017). Experimental demonstrations of noise-robustness of compression-based joint wavelet transform correlator in retinal recognition. Optics - International Journal for Light and Electron Optics, 142, 168-173. CrossRef
Go back to reference Boixl, M., & Cantó, B. (2010). Wavelet transform application to the compression of images. Mathematical and Computer Modeling, 52 (7), 1265-1270. MathSciNetMATHCrossRef Boixl, M., & Cantó, B. (2010). Wavelet transform application to the compression of images. Mathematical and Computer Modeling, 52 (7), 1265-1270. MathSciNetMATHCrossRef
Go back to reference Jan, F., Usman, I., & Agha, S. (2012). Iris localization in frontal eye images for less constrained iris recognition systems. Digital Signal Processing, 22 (6), 971-986. MathSciNetCrossRef Jan, F., Usman, I., & Agha, S. (2012). Iris localization in frontal eye images for less constrained iris recognition systems. Digital Signal Processing, 22 (6), 971-986. MathSciNetCrossRef
Go back to reference Wild, P., Ferryman, J., & Uhl, A. (2015). Impact of (segmentation) quality on long vs. short-timespan assessments in iris recognition performance. IET Biometrics, 4 (4), 227-235. CrossRef Wild, P., Ferryman, J., & Uhl, A. (2015). Impact of (segmentation) quality on long vs. short-timespan assessments in iris recognition performance. IET Biometrics, 4 (4), 227-235. CrossRef
Go back to reference Soliman, N. F., Mohamed, E., Magdi, F., Abd El-Samie, F. E., & Abd Elnaby, M. (2017). Efficient iris localization and recognition. Optics, 140, 469-475. CrossRef Soliman, N. F., Mohamed, E., Magdi, F., Abd El-Samie, F. E., & Abd Elnaby, M. (2017). Efficient iris localization and recognition. Optics, 140, 469-475. CrossRef
Go back to reference Chirchi, V. R. E., & Waghmare, L. M. (2017). Enhanced isocentric segmenter and wavelet rectangular coder to iris segmentation and recognition. International Journal of Intelligent Engineering & Systems, 2017, 1–10. CrossRef Chirchi, V. R. E., & Waghmare, L. M. (2017). Enhanced isocentric segmenter and wavelet rectangular coder to iris segmentation and recognition. International Journal of Intelligent Engineering & Systems, 2017, 1–10. CrossRef
Go back to reference Chirchi, E. R. M., & Digambar, K. R. (2017). Modified circular fuzzy segmenter and local circular encoder to iris segmentation and recognition. International Journal of Intelligent Engineering & Systems, 10 (2), 183-192. Chirchi, E. R. M., & Digambar, K. R. (2017). Modified circular fuzzy segmenter and local circular encoder to iris segmentation and recognition. International Journal of Intelligent Engineering & Systems, 10 (2), 183-192.
Go back to reference Naguru, I., & Rao, N. K. (2017). Feature matching in iris recognition system using MATLAB. International Journal on Advanced Science Engineering Information Technology, 7 (5), 1748-1757. CrossRef Naguru, I., & Rao, N. K. (2017). Feature matching in iris recognition system using MATLAB. International Journal on Advanced Science Engineering Information Technology, 7 (5), 1748-1757. CrossRef
Go back to reference Duda, R. O., & Hart, P. E. (1972). Use of the hough transformation to detect lines and curves in pictures. Communications of the ACM, 15 (1), 11-15. MATH Cross Ref Duda, R. O., & Hart, P. E. (1972). Use of the hough transformation to detect lines and curves in pictures. Communications of the ACM, 15 (1), 11-15. MATHCrossRef
Go back to reference Cherabit, N., Chelali, F. Z., & Djeradi, A. (2012). Circular Hough transform for iris localization. Science and Technology, 2 (5), 114-121. CrossRef Cherabit, N., Chelali, F. Z., & Djeradi, A. (2012). Circular Hough transform for iris localization. Science and Technology, 2 (5), 114-121. CrossRef
Go back to reference Proença, H., & Alexandre, L. A. (2005). UBIRIS: A noisy iris image database. In 13th international conference on image analysis and processing, Italy, 2005 (pp. 970-977). Proença, H., & Alexandre, L. A. (2005). UBIRIS: A noisy iris image database. In 13th international conference on image analysis and processing, Italy, 2005 (pp. 970-977).
Go back to reference Dillak, R. Y., & Bintiri, M. G. (2016). A novel approach for iris recognition. In IEEE region 10 symposium (TENSYMP), Bali, 2016 (pp. 231–236). Dillak, R. Y., & Bintiri, M. G. (2016). A novel approach for iris recognition. In IEEE region 10 symposium (TENSYMP), Bali, 2016 (pp. 231–236).
Go back to reference Han, W.-Y., Chen, W.-K., Lee, Y.-P., Wu, K.-S., & Lee, J.-C. (2014). Iris recognition based on local mean decomposition. Applied Mathematics & Information Sciences An International Journal, 8 (2), 217-222. CrossRef Han, W.-Y., Chen, W.-K., Lee, Y.-P., Wu, K.-S., & Lee, J.-C. (2014). Iris recognition based on local mean decomposition. Applied Mathematics & Information Sciences An International Journal, 8 (2), 217-222. CrossRef
Go back to reference Elgamal, M., & Al-biqami, N. (2013). An efficient feature extraction method for iris recognition based on wavelet transformation. International Journal of Information Technology, 2, 521-527. Elgamal, M., & Al-biqami, N. (2013). An efficient feature extraction method for iris recognition based on wavelet transformation. International Journal of Information Technology, 2, 521-527.
Go back to reference Umer, S., Dhara, B. C., & Chanda, B. (2016).Texture code matrix-based multi-instance iris recognition. Pattern Analysis and Applications, 19 (1), 283-290. MathSciNetCrossRef Umer, S., Dhara, B. C., & Chanda, B. (2016). Texture code matrix-based multi-instance iris recognition. Pattern Analysis and Applications, 19 (1), 283-290. MathSciNetCrossRef
About this article
title
Compression-Robust and Fuzzy-Based Feature-Fusion Model for Optimizing the Iris Recognition
Authors:
Kapil Juneja
Chhavi Rana
Publication date
09.08.2020
DOI
https://doi.org/10.1007/s11277-020-07714-3