IMPROVING IRIS RECOGNITION ACCURACY USING GABOR KERNELS WITH NEAR-HORIZONTAL ORIENTATIONS
DOI:
https://doi.org/10.29284/ijasis.8.1.2022.25-39Keywords:
Biometrics, iris recognition, Gabor filter, iris coding, iris matching.Abstract
Gabor filter has proven to be one of the most successful techniques for the extraction of iris features. However, the selection of Gabor kernel orientations remains an important key for an optimum performance. In this paper, Gabor kernels with horizontal and two near-horizontal orientations (0°, 15°, 165°) are used for extracting iris features, as an attempt to improve the performance of iris recognition rate. The three iris features are cascaded into one image for the purpose of image matching. To this end, an iris recognition system is developed using methods of iris localization, eyelid removal and iris matching by Hamming Distance (HD) that have been developed previously by the same authors. The system is implemented on three standard datasets, CASIA-1, CASIA-Lamp-4 and SDUMLA-HMT. The results have shown that the overall accuracy (Accu), the True Positive Rate (TPR) and the Equal Error Rate (EER) EER are (98.85%, 99.42% and 0.58%) for CASIA-1 database, (96.56%, 98.25% and 1.75%) for CASIA-Lamp-4 database and (96%, 98% and 2.0%) for SDUMLA-HMT database. These results outrage the results of most of the previous works that have used the same databases.
References
A. K. Jain, K. Nandakumar and A. Nagar, “Biometric Template Security,” EURASIP Journals on Advances in Signal Processing, Vol. 2008, 2008, pp. 1–17.
B. Hamdan and K. Mokhtar, “Face Recognition Using Angular Radial Transform,” Journal of King Saud University – Computer and Information Sciences, vol. 30, no. 2, 2018, pp. 141-151.
J. Daugman, “New Methods in Iris Recognition,” IEEE Transactions on Systems, Man, And Cybernetics—Part B: Cybernetics, vol. 37, no. 5, 2007, pp. 1167-1175.
A. K. Jain, K. Nandakumar and A. Ross, “50 Years of Biometric Research: Accomplishments, Challenges, and Opportunities,” Pattern Recognition Letters, vol. 70, 2016, pp. 80-105.
A. I. Desoky, H. A. Ali and N. B. Abdel-Hamid, “Enhancing Iris Recognition System Performance,” International Conference on Computer Engineering & Systems”, 2010, pp. 21-26.
A. A. Mustafa and A. AK. Tahir, “Improving the Performance of Finger vein Recognition System Using a New Scheme of Modified Pre-processing Methods,” Academic Journal of Nawroz University, vol. 9, no. 3, 2020, pp. 397-409.
A. AK. Tahir, S. S. Dawood and S. Anghelus, “An Iris Recognition System Based On A New Method Of Iris Localization,” International Journal of Open Information Technologies, vol. 9, no. 7, 2021, pp. 67-76.
H. K. Rana, S. Azam, M. R. Akhtar, J. M. Quinn and M. A. Moni, “A Fast Iris Recognition System Through Optimum Feature Extraction,” Peer Journal of Computer Science, vol. 5, No. e184, 2019, pp. 1-13.
A. A. Mustafa, and A. AK. Tahir, “A New Finger-Vein Recognition System Using the Complete Local Binary Pattern and the Phase Only Correlation,” International Journal on Signal and Image Sciences, vol. 7, no. 1, 2021, pp. 38-56.
N. Kanjan, K. Patil, S. Ranaware and P.A Sarokte, “A Comparative Study of Fingerprint Matching Algorithms,” International Research Journal of Engineering and Technology, vol. 4, no. 11, 2017, pp. 1892-1896.
V. R. Gosavi, G.S. Sable, K. Anil, et al., “Evaluation of Feature Extraction Techniques using Neural Network as a Classifier: A Comparative Review for face Recognition,” vol. 4, no. 2, 2018, pp. 1082-1091.
L. Fei, G. Lu, W. Jia, et al, “Feature Extraction Methods for Palmprint Recognition: A Survey and Evaluation,” IEEE Transactions on Systems Man, and Cybernetics, vol. 49, no. 2, 2019, pp. 346-363.
X. Li, Y. Makihara, C. Xu, Y. Yagi and M. Ren, “Gait Recognition Invariant to Carried Objects Using Alpha Blending Generative Adversarial Networks,” vol. 105, 2020, pp. 1-12.
P. D. Videkar and K.S. Ingle, “Finger Vein Identification Based on Minutiae Feature Extraction with Spurious Minutiae Removal,” International Research Journal of Engineering and Technology, vol. 4, no. 4, 2017, pp.3403-3406.
S. Biswas and J. Sil, “An Efficient Face Recognition Method Using Contourlet and Curvelet Transform,” Journal of King Saud University – Computer and Information Sciences, vol. 32, 2020, pp. 718-729.
C. S. S. Anupama and P. Rajesh, “Authentication Using Iris,” International Journal of Innovations in Engineering and Technology, vol. 2, no. 4, 2013, pp. 126-138.
J. G. Daugman, “High Confidence Visual Recognition of Persons by A Test of Statistical Independence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, 1993, pp. 1148-1161.
R. P. Wildes, “Iris Recognition: An Emerging Biometric Technology,” Proceedings of the IEEE, vol. 85, no. 9, 1997, pp. 1348-1363.
S. Minhas and M. Y. Javed, “Iris Feature Extraction Using Gabor Filter,” International Conference on Emerging Technologies, 2009, pp. 252-255.
A. A. Tahir and S. Anghelus, “An Accurate and Fast Method for Eyelid Detection,” International Journal of Biometrics, vol. 12, no. 2, 2020, pp. 163-178.
C. Sanchez-Avila and R. Sanchez-Reillo, “Two Different Approaches for Iris Recognition Using Gabor Filters and Multiscale Zero-Crossing Representation,” Pattern Recognition, vol. 38, no. 2, 2005, pp. 231-240.
R. Y. F. Ng, Y. H. Tay and K. M. Mok, “Iris Recognition Algorithms Based on Texture Analysis”, International Symposium on Information Technology, 2008, pp. 1-5.
A. A. Ibrahim, “Iris Recognition Encoding Using Gabor Filters,” Al-Taqani, vol. 21, no. 6, 2008, pp. 1-10.
R. Shanthi and B. Dinesh, “Iris Based Authentication System”, IOSR Journal of Engineering, vol. 3, no. 4, 2013, pp. 15-20
V. Saravanan and R. Sindhuja, “Iris Authentication through Gabor Filter Using DSP Processor,” IEEE Conference on Information and Communication Technologies, 2013, pp. 568-571.
H. Rai and A. Yadav, “Iris Recognition Using Combined Support Vector Machine and Hamming Distance Approach,” Expert Systems with Applications, vol. 41, 2014, pp. 588-593.
R. Vyas, T. Kanumuri and G. Sheoran, “An Approach for Iris Segmentation in Constrained Environments,” Nature Inspired Computing Advances in Intelligent Systems and Computing, 2018, pp. 99-107.
R. Sharma and V. Mohan, “Iris Recognition Using Gabor Filters Optimized by Genetic Algorithm and Particle Swarm Optimization,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 5, 2016, pp. 726-729.
S. G. Firake and P. M. Mahajan, “Comparison of Iris Recognition Using Gabor Wavelet, Principal Component Analysis and Independent Component Analysis,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 4, no. 6, 2016, pp. 12334-12342.
T. Y. Chai, B. M. Goi, Y. H. Tay and W. J. Nyee, “A Trainable Method for Iris Recognition Using Random Feature Points,” Conference on Technologies and Applications of Artificial Intelligence, 2017, pp. 142-147.
N. Ahmadi, and G. Akbarizadeh, “Hybrid Robust Iris Recognition Approach Using Iris Image Pre-Processing, Two-Dimensional Gabor Features and Multi-Layer Perceptron Neural Network/PSO,” IET Biometrics, vol. 7, no. 2, 2018, pp. 153-162.
S. Salve, “Iris Recognition Using Wavelet Transform and SVM Based Approach,” Asian Journal of Convergence in Technology, vol. V, no. I, 2019, pp. 1-9.
V. Velisavljevic, “Low-Complexity Iris Coding and Recognition Based on Directionlets,” IEEE Transactions on Information Forensics and Security, vol. 4, no. 3, 2009, pp. 410-417.
A. Pranith and C. R. Srikanth, “Iris Recognition Using Corner Detection,” 2nd International Conference on Information Science and Engineering, 2010, pp. 2151-2154.
E. Mattar, “Principal Components Analysis Based Iris Recognition and Identification System,” International Journal of Soft Computing and Engineering, vol. 3, no. 2, 2013, pp. 430-436.
M. B. Ashwini, I. Mohammad and A. Fawaz, “Evaluation of Iris Recognition System on Multiple Feature Extraction Algorithms and its Combinations,” International Journal of Computer Applications Technology and Research, vol. 4, no. 8, 2015, pp. 592 – 598.
S. Umer, B. C. Dhara, and B. Chanda, “An Iris Recognition System Based on Analysis of Textural Edgeness Descriptors,” IETE Technical Review, vol. 35, no. 2, 2017, pp. 1-12.
M. Hamd and S. K. Ahmed, “Biometric System Design for Iris Recognition Using Intelligent Algorithms,” International Journal of Modern Education and Computer Science, vol. 10, no. 3, 2018, pp. 9-16.
E. Abdulmunem and S. H. Abbas, “Iris Recognition Using SVM and BP Algorithms”, International Journal of Engineering Research and Advanced Technology, vol. 4, no. 5, 2018, pp. 30-37.
H. Wei, Y. Li and C. T. Li, “Effective Extraction of Gabor Features for Adaptive Mammogram Retrieval", IEEE International Conference on Multimedia and Expo, 2007, pp. 1503-1506.
P. J. Phillips, K. W. Bowyer and P. J. Flynn, “Comments on The CASIA Version 1.0 Iris Data Set”, IEEE on Pattern Analysis and Machine Intelligence, vol. 29, no. 10, 2007, pp. 1869-1870.
Y. Yin, L. Liu and X. Sun, “SDUMLA-HMT: A Multimodal Biometric Database”, In Sun, Z., Lai, J., Chen, X. and Tan, T. (Eds.): ‘Biometric Recognition, 2011, pp. 260–268.
Center for Biometrics and security Research, “Notes on CASIA-IrisV4”, Available: http://www.cbsr.ia.ac.cn/china/Iris%20Databases%20CH.asp.
B. Hamdan and K. Mokhtar, “Face Recognition Using Angular Radial Transform,” Journal of King Saud University – Computer and Information Sciences, vol. 30, no. 2, 2018, pp. 141-151.
J. Daugman, “New Methods in Iris Recognition,” IEEE Transactions on Systems, Man, And Cybernetics—Part B: Cybernetics, vol. 37, no. 5, 2007, pp. 1167-1175.
A. K. Jain, K. Nandakumar and A. Ross, “50 Years of Biometric Research: Accomplishments, Challenges, and Opportunities,” Pattern Recognition Letters, vol. 70, 2016, pp. 80-105.
A. I. Desoky, H. A. Ali and N. B. Abdel-Hamid, “Enhancing Iris Recognition System Performance,” International Conference on Computer Engineering & Systems”, 2010, pp. 21-26.
A. A. Mustafa and A. AK. Tahir, “Improving the Performance of Finger vein Recognition System Using a New Scheme of Modified Pre-processing Methods,” Academic Journal of Nawroz University, vol. 9, no. 3, 2020, pp. 397-409.
A. AK. Tahir, S. S. Dawood and S. Anghelus, “An Iris Recognition System Based On A New Method Of Iris Localization,” International Journal of Open Information Technologies, vol. 9, no. 7, 2021, pp. 67-76.
H. K. Rana, S. Azam, M. R. Akhtar, J. M. Quinn and M. A. Moni, “A Fast Iris Recognition System Through Optimum Feature Extraction,” Peer Journal of Computer Science, vol. 5, No. e184, 2019, pp. 1-13.
A. A. Mustafa, and A. AK. Tahir, “A New Finger-Vein Recognition System Using the Complete Local Binary Pattern and the Phase Only Correlation,” International Journal on Signal and Image Sciences, vol. 7, no. 1, 2021, pp. 38-56.
N. Kanjan, K. Patil, S. Ranaware and P.A Sarokte, “A Comparative Study of Fingerprint Matching Algorithms,” International Research Journal of Engineering and Technology, vol. 4, no. 11, 2017, pp. 1892-1896.
V. R. Gosavi, G.S. Sable, K. Anil, et al., “Evaluation of Feature Extraction Techniques using Neural Network as a Classifier: A Comparative Review for face Recognition,” vol. 4, no. 2, 2018, pp. 1082-1091.
L. Fei, G. Lu, W. Jia, et al, “Feature Extraction Methods for Palmprint Recognition: A Survey and Evaluation,” IEEE Transactions on Systems Man, and Cybernetics, vol. 49, no. 2, 2019, pp. 346-363.
X. Li, Y. Makihara, C. Xu, Y. Yagi and M. Ren, “Gait Recognition Invariant to Carried Objects Using Alpha Blending Generative Adversarial Networks,” vol. 105, 2020, pp. 1-12.
P. D. Videkar and K.S. Ingle, “Finger Vein Identification Based on Minutiae Feature Extraction with Spurious Minutiae Removal,” International Research Journal of Engineering and Technology, vol. 4, no. 4, 2017, pp.3403-3406.
S. Biswas and J. Sil, “An Efficient Face Recognition Method Using Contourlet and Curvelet Transform,” Journal of King Saud University – Computer and Information Sciences, vol. 32, 2020, pp. 718-729.
C. S. S. Anupama and P. Rajesh, “Authentication Using Iris,” International Journal of Innovations in Engineering and Technology, vol. 2, no. 4, 2013, pp. 126-138.
J. G. Daugman, “High Confidence Visual Recognition of Persons by A Test of Statistical Independence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, 1993, pp. 1148-1161.
R. P. Wildes, “Iris Recognition: An Emerging Biometric Technology,” Proceedings of the IEEE, vol. 85, no. 9, 1997, pp. 1348-1363.
S. Minhas and M. Y. Javed, “Iris Feature Extraction Using Gabor Filter,” International Conference on Emerging Technologies, 2009, pp. 252-255.
A. A. Tahir and S. Anghelus, “An Accurate and Fast Method for Eyelid Detection,” International Journal of Biometrics, vol. 12, no. 2, 2020, pp. 163-178.
C. Sanchez-Avila and R. Sanchez-Reillo, “Two Different Approaches for Iris Recognition Using Gabor Filters and Multiscale Zero-Crossing Representation,” Pattern Recognition, vol. 38, no. 2, 2005, pp. 231-240.
R. Y. F. Ng, Y. H. Tay and K. M. Mok, “Iris Recognition Algorithms Based on Texture Analysis”, International Symposium on Information Technology, 2008, pp. 1-5.
A. A. Ibrahim, “Iris Recognition Encoding Using Gabor Filters,” Al-Taqani, vol. 21, no. 6, 2008, pp. 1-10.
R. Shanthi and B. Dinesh, “Iris Based Authentication System”, IOSR Journal of Engineering, vol. 3, no. 4, 2013, pp. 15-20
V. Saravanan and R. Sindhuja, “Iris Authentication through Gabor Filter Using DSP Processor,” IEEE Conference on Information and Communication Technologies, 2013, pp. 568-571.
H. Rai and A. Yadav, “Iris Recognition Using Combined Support Vector Machine and Hamming Distance Approach,” Expert Systems with Applications, vol. 41, 2014, pp. 588-593.
R. Vyas, T. Kanumuri and G. Sheoran, “An Approach for Iris Segmentation in Constrained Environments,” Nature Inspired Computing Advances in Intelligent Systems and Computing, 2018, pp. 99-107.
R. Sharma and V. Mohan, “Iris Recognition Using Gabor Filters Optimized by Genetic Algorithm and Particle Swarm Optimization,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 5, 2016, pp. 726-729.
S. G. Firake and P. M. Mahajan, “Comparison of Iris Recognition Using Gabor Wavelet, Principal Component Analysis and Independent Component Analysis,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 4, no. 6, 2016, pp. 12334-12342.
T. Y. Chai, B. M. Goi, Y. H. Tay and W. J. Nyee, “A Trainable Method for Iris Recognition Using Random Feature Points,” Conference on Technologies and Applications of Artificial Intelligence, 2017, pp. 142-147.
N. Ahmadi, and G. Akbarizadeh, “Hybrid Robust Iris Recognition Approach Using Iris Image Pre-Processing, Two-Dimensional Gabor Features and Multi-Layer Perceptron Neural Network/PSO,” IET Biometrics, vol. 7, no. 2, 2018, pp. 153-162.
S. Salve, “Iris Recognition Using Wavelet Transform and SVM Based Approach,” Asian Journal of Convergence in Technology, vol. V, no. I, 2019, pp. 1-9.
V. Velisavljevic, “Low-Complexity Iris Coding and Recognition Based on Directionlets,” IEEE Transactions on Information Forensics and Security, vol. 4, no. 3, 2009, pp. 410-417.
A. Pranith and C. R. Srikanth, “Iris Recognition Using Corner Detection,” 2nd International Conference on Information Science and Engineering, 2010, pp. 2151-2154.
E. Mattar, “Principal Components Analysis Based Iris Recognition and Identification System,” International Journal of Soft Computing and Engineering, vol. 3, no. 2, 2013, pp. 430-436.
M. B. Ashwini, I. Mohammad and A. Fawaz, “Evaluation of Iris Recognition System on Multiple Feature Extraction Algorithms and its Combinations,” International Journal of Computer Applications Technology and Research, vol. 4, no. 8, 2015, pp. 592 – 598.
S. Umer, B. C. Dhara, and B. Chanda, “An Iris Recognition System Based on Analysis of Textural Edgeness Descriptors,” IETE Technical Review, vol. 35, no. 2, 2017, pp. 1-12.
M. Hamd and S. K. Ahmed, “Biometric System Design for Iris Recognition Using Intelligent Algorithms,” International Journal of Modern Education and Computer Science, vol. 10, no. 3, 2018, pp. 9-16.
E. Abdulmunem and S. H. Abbas, “Iris Recognition Using SVM and BP Algorithms”, International Journal of Engineering Research and Advanced Technology, vol. 4, no. 5, 2018, pp. 30-37.
H. Wei, Y. Li and C. T. Li, “Effective Extraction of Gabor Features for Adaptive Mammogram Retrieval", IEEE International Conference on Multimedia and Expo, 2007, pp. 1503-1506.
P. J. Phillips, K. W. Bowyer and P. J. Flynn, “Comments on The CASIA Version 1.0 Iris Data Set”, IEEE on Pattern Analysis and Machine Intelligence, vol. 29, no. 10, 2007, pp. 1869-1870.
Y. Yin, L. Liu and X. Sun, “SDUMLA-HMT: A Multimodal Biometric Database”, In Sun, Z., Lai, J., Chen, X. and Tan, T. (Eds.): ‘Biometric Recognition, 2011, pp. 260–268.
Center for Biometrics and security Research, “Notes on CASIA-IrisV4”, Available: http://www.cbsr.ia.ac.cn/china/Iris%20Databases%20CH.asp.
Downloads
Published
2022-06-30
How to Cite
Ahmed AK. Tahir, & Steluta Anghelus. (2022). IMPROVING IRIS RECOGNITION ACCURACY USING GABOR KERNELS WITH NEAR-HORIZONTAL ORIENTATIONS. INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, 8(1), 25–39. https://doi.org/10.29284/ijasis.8.1.2022.25-39
Issue
Section
Articles
License
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
