An Iris Recognition System Using A New Method of Iris Localization

Ahmed AK. Tahir, Sarhan S. Dawood, Steluta Anghelus

Abstract


Abstract—An iris recognition system for person identification is developed with a new method for iris localization. For pupil boundary detection, a method robust to the specular point reflection problem is developed. It consists of a morphological filter and two-direction scanning methods. For limbic boundary detection, the Wildes method is modified by restricting the process of Canny edge detector and Hough transform to a small Region-Of-Interest (ROI) not exceeding 20% of the image size. For eyelid detection, the method of Refine-Connect-Extend-Smooth (R-C-E-S) is used, which detects three possible cases (single eyelid, both eyelids, and free iris). For iris normalization, rubber-sheet model transform is used and for iris coding, the Gabor filter is used. The performance of the system is evaluated for the individual stages and for the whole system using three different databases (CASIA-V1.0, CASIA-V4.0-Lamp, and SDUMLA-HMT). The accuracy of correct detection reached 99.9%-100% for pupil boundary and 99.6%-99.9% for limbic boundary detection. For eyelid detection; the accuracy reached 93.2%-97.6% for the upper eyelid, 95.3%-99.15% for the lower eyelid, and 96.7%-96.92% for free iris (iris not occluded by eyelids). The overall accuracy and the Equal Error Rate (EER) of the system for the CASIA-V1.0 database are 96.48% and 1.76%, for CASIA-V4.0-Lamp, are 95.1% and 2.45%, and for SDUMLA-HMT are 93.6% and 3.2%.


Full Text:

PDF

References


REFERENCES

A. K. Jain, K. Nandakumar, and A. Nagar, “Biometric template security,” EURASIP J. Adv. Signal Process, Vol. 2008, pp. 113–129.

B. Hamdan, and K. Mokhtar, “Face recognition using Angular Radial Transform,” Journal of King Saud University – Computer and Information Sciences, 30, 2018, pp. 141-151.

A. K. Jain, A. Ross, and S. Prabhakar, “An Introduction to Biometric Recognition,” In Circuits and Systems for Video Technology. IEEE Transactions on, 14(1), 2004, pp. 4-20.

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.

N. Kanjan, K. Patil, S. Ranaware, et al., “A Comparative Study of Fingerprint Matching Algorithms,” International Research Journal of Engineering and Technology (IRJET), 4(11), 2017, pp. 1892-1896.

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 (IRJET), Vol. 4, No. 4, 2017, pp.3403-3406.

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,” IJSRST, Vol. 4, No. 2, 2018, pp. 1082-1091.

S. Biswas, and J. Sil, “An efficient face recognition method using contourlet and curvelet Transform,” Journal of King Saud University – Computer and Information Sciences, 32, 2020, pp. 718-729.

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: Systems, Vol. 49, No. 2, 2019, pp. 346-363, doi: 10.1109/TSMC.2018.2795609.

A. AK. Tahir, and S. Anghelus, “An accurate and fast method for eyelid detection. International Journal of Biometrics (IJBM), Vol. 12, No. 2, 2020, pp. 163-178.

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 (AJNU), Vol. 9, No. 3, 2020, pp. 397-409, https://doi.org/10.25007/ajnu.v9n3a855.

C.S.S. Anupama, P. Rajesh, “Authentication Using Iris,” International Journal of Innovations in Engineering and Technology (IJIET), Vol. 2, No. 4, 2013, pp. 126-138.

J.G. Daugman, “High Confidence Visual Recognition of Persons by A Test of Statistical Independence,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 15, No. 11, 1993, pp. 1148-1161.

R. P. Wildes, “Iris Recognition: An Emerging Biometric Technology,” In Proceedings of the IEEE, Vol. 85, No. 9, 1997, pp. 1348-1363.

S. P. Narote, A. S. Narote, L.M. Waghmare, et al., “An Automated Segmentation Method for Iris Recognition,” IEEE TENCON , Region 10 Conference, Hong Kong, 1-4,2006, doi: 10.1109/TENCON.2006.344211.

A. Ferreira, A. Lourenço, B.P. Pinto, et al., “Modifications and Improvements on Iris Recognition,” Proc INSTICC International Conference on Bio-inspired Systems and Signal Processing -Biosignals, Porto, Portugal, Vol. 1, 2009, pp. 72 – 79.

P. Yao, J. Li, X. Ye, et al., “Iris Recognition Algorithm Using Modified Log-Gabor Filters. In Pattern Recognition,” 2006 ICPR, 18th IEEE International Conference on, 2006, pp. 461-464.

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. Desoky, H. Ali, and N. B.Abdel-Hamid, “Enhancing Iris Recognition System Performance,” In Computer Engineering and Systems (ICCES), IEEE International Conference on,2010, pp. 21-26.

B. C. Kovoor, M. H. Supriya, K. P. Jacob, “Iris Biometric Recognition System Employing Canny Operator,” In Computer Science & Information Technology (CS & IT), 2013, pp. 65-74.

R. Y. F. Ng, Y. H. Tay, and K. M. Mok, “Iris Recognition Algorithms Based on Texture Analysis,” In Information Technology, (ITSim 2008), International Symposium on, pp. 1-5.

A. Pranith, and C.R. Srikanth, “Iris recognition using corner detection,” The 2nd International Conference on Information Science and Engineering, Hangzhou, 2010, pp. 2151-2154.

N. Singh, D. Gandhi, and K. P. Singh, “Iris Recognition System Using A Canny Edge Detection and A Circular Hough Transform. International Journal of Advances in Engineering & Technology, Vol. 1, No. 2, 2011, pp. 221-228.

A.K. Dewangan, and M. A. Siddhiqui, “Human Identification and Verification Using Iris Recognition by Calculating Hamming Distance," International Journal of Soft Computing and Engineering, (IJSCE), Vol. 2, No. 2,2012, pp. 334-338.

S. S. Mabrukar, N. S. Sonawane, and J.A. Bagban, “Biometric System Using Iris Pattern Recognition,” In International Journal of Innovative Technology and Exploring Engineering, Vol. 2, No. 5, 2013, pp. 54-57.

R. Shanthi, and B. Dinesh, “Iris Based Authentication System,” IOSR Journal of Engineering (IOSRJEN), Vol. 3, No. 4, 2013, pp. 15-20.

T. Chai, B. Goi, Y. H. Tay, et al., “A Trainable Method For Iris Recognition Using Random Feature Points,” Conference on Technologies and Applications of Artificial Intelligence (TAAI), Taipei, 2017, pp. 142-147.

J. Cui, Y. Wang, J. Huang, et al., “An Iris Image Synthesis Method Based On PCA and Super-Resolution,” In Pattern Recognition, (ICPR), Proceedings of the 17th IEEE International Conference on, 2004, pp. 471-474.

K. Miyazawa K.. Ito T. Aoki, et al., “An Iris Recognition System Using Phase-Based Image Matching,” In Proceedings of IEEE International Conference on Image Processing, (ICIP’06), 2006, pp. 325-328.

V. Conti, G. Milici, F. Sorbello, et al., “A Novel Iris Recognition System Based on Micro-Features,” IEEE Workshop on Automatic Identification Advanced Technologies, Alghero, 2007, pp. 253-258, doi: 10.1109/AUTOID.2007.380629.

L. L. Ling, and D. F. de Brito, “Fast and Efficient Iris Image Segmentation,” Journal of Medical and Biological Engineering, Vol. 30, No. 6, 2010, pp. 381-392.

Y. Du, C. Belcher, and Z. Zhou, “Scale invariant Gabor descriptor-based non-cooperative iris recognition,” EURASIP J. Adv. Signal Process., 2010: pp. 1–13.

Gomai, A. El-Zaart, H. Mathkour, “A New Approach for Pupil Detection in Iris Recognition System,” The 2nd International Conference on Computer Engineering and Technology, 2010, pp. V4-415- V4-419.

E. Mattar, “Principal Components Analysis Based Iris Recognition and Identification System,” International Journal of Soft Computing and Engineering (IJSCE), Vol. 3, No. 2, 2013, pp. 430-436.

S. Shah, and A. Ross, “Iris Segmentation Using Geodesic Active Contours,” IEEE Transactions on Information Forensics And Security, Vol. 4, No. 4, 2009, pp. 824-836.

M. A. Abdullah, S. S. Dlay, et al., “Fast and Accurate Pupil Isolation Based on Morphology and Active Contour,” International Journal of Information and Electronics Engineering, Vol. 4, No. 6, 2014, pp. 418-422.

M. A. Abdullah, S. S. Dlay, W. L. Woo, et al., “Robust iris segmentation method based on a new active contour force with a noncircular normalization,” IEEE Trans. Syst. Man Cybern. Syst., Vol. 47, 2017, pp. 3128–3141. [CrossRef]

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. 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, DOI: 10.15680/IJIRCCE.2016. 0406293.

S. Umer, B. C. Dhara, and B. Chanda, “An Iris Recognition System Based on Analysis of Textural Edgeness Descriptors,” IETE Tech. Rev., Vol. 35, No. 2, 2017, pp. 1-12.

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.

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, DOI: 10.5815/ijmecs.2018.03.02.

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 Biom., Vol. 7, No. 2, 2018, pp. 153-162.

E. Abdulmunem, and S. H. Abbas, “Iris recognition using SVM and BP algorithms,” International Journal of Engineering Research and Advanced Technology (IJERAT), Vol. 4, No. 5, 2018, pp. 30-37.

S. Salve, “Iris Recognition Using Wavelet Transform and SVM Based Approach,” Asian Journal of Convergence in Technology, Vol. V, Issue I, 2019, pp. 1-9. Available: http://www.asianssr.org/index.php/ajct/article/view/725

H. K. Rana, S. Azam, M. R. Akhtar, et al., “A fast iris recognition system through optimum feature extraction,” Peer J Comput. Sci. Vol. 5, No. e184, 2019, pp. 1-13, https://doi.org/10.7717/peerj-cs.184.

S. Nanayakkara, and R. G. M. Meegama, “A Review of Literature on Iris Recognition,” International Journal of Research, Vol. IX, Issue II, 2020, pp. 106-120.

AS. Al-Waisy R. Qahwaji S. Ipson, et al., “A fast and accurate iris localization technique for healthcare security system,” In 2015 IEEE international conference computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing, Liverpool, UK, 2015: 1028–1034, DOI: 10.1109/CIT/IUCC/DASC/PICOM.2015.156

AS. Al-Waisy R. Qahwaji S. Ipson, et al., “A multi‑biometric iris recognition system based on a deep learning approach,” Pattern Analysis and Applications, 2017, pp. 1-20, DOI 10.1007/s10044-017-0656-1

Y. W. Lee, K. W. Kim, T. M. Hoang, et al., “Deep Residual CNN-Based Ocular Recognition Based on Rough Pupil Detection in the Images by NIR Camera Senso,” Sensors, Vol. 19, No. 842, 2019: pp. 1-30, doi:10.3390/s19040842. [CrossRef]

Y. H. Li, P. J. Huang, and Y. Juan, “An Efficient and Robust Iris Segmentation Algorithm Using Deep Learning,” Mobile Information Systems, 2019, pp. 1 – 14, doi.org/10.1155/2019/4568929.

A. I. Mohammed, and A. AK. Tahir, “A New Image Classification System Using Deep Convolution Neural Network And Modified Amsgrad Optimizer,” Journal of University of Duhok, Vol. 22, No. 2 (Pure and Eng. Sciences), 2019, pp. 89-101, DOI: https://doi.org/10.26682/sjuod.2019.22.2.10.

A. I. Mohammed, and A. AK. Tahir, “A New Optimizer for Image Classification using Wide ResNet (WRN),” Academic Journal of Nawroz University (AJNU), Vol. 9, No. 4, 2020, pp. 1-13, https://doi.org/10.25007/ajnu.v9n3a855.

A. AK. Tahir, and A. I. Bindian, “Localizarea Irisului Pentru Sistemul Biometric De Identificare A Ersoanelor,” The XVIII International Conference on Multidisciplinary, ‘Professor Dorin Paul - Romanian hydropower founder’, Sebes, Romania, June-2016, in the Romanian Journal of Sceince and Engineering, Revista "ȘTIINȚĂ ȘI INGINERIE", Vol. 30/2016, AGIR Edition, ISSN 2067-7138, 2016, 215-224. (Conference Proceedings in Romanian and English Languages).

J. Daugman “The Importance of Being Random: Statistical Principles of Iris Recognition,” The Journal of Pattern Recognition, Vol. 36, 2003, pp. 279-291.

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.

S. Dey, and D. Samanta, “A Novel Approach to Iris Localization for Iris Biometric Processing,” International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, Vol. 1, No. 5, 2007, pp. 293-304.

R. D. Labati, A. Genovese, and V. Piuri, “Iris segmentation: State of the art and innovative methods,” In Cross Disciplinary Biometric Systems. Springer, 2012, pp. 151–182.

L. Jiang, Y. Zhang, and W. Li, “A Novel Iris Location Method for Fast Iris Recognition,” In Image and Signal Processing, (CISP'09), 2nd International Congress on, 2009, pp. 1-5.

M. Essam, M. Abd Elnaby, M. Fikri, et al., “A fast accurate algorithm for iris localization using a coarse-to-fine approach,” In Japan-Egypt Conference on Electronics, Communications and Computers (JEC-ECC), Alexandria, Egypt, 2012, pp. 63–67.

S. M. Ali, “Person Identification Technique Using Human Iris Recognition,” International Journal of Computer Technology and Electronics Engineering (IJCTEE), Vol. 3, No. 6, 2013, pp. 1-5.

R. B. Dubey, and A. Madan, “Iris Localization Using Daugman's Intero-Differential Operator. International Journal of Computer Applications, Vol. 93, No. 3, 2014, pp. 6-12.

W. Chen, K. Chih, S. Shih, et al., “Personal Identification Technique based on Human Iris Recognition with Wavelet Transform,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, (ICASSP '05), Philadelphia, Pa, USA, 2005, pp. 949-952.

T. Tan, Z. He, and Z. Sun, “Efficient and Robust Segmentation of Noisy Iris Images for Non-cooperative Iris Recognition,” Image and Vision Computing, Vol.28, 2010, pp. 223–230.

F. He, Y. Liu, X. Zhu, et al., “Multiple local feature representations and their fusion based on an SVR model for iris recognition using optimized Gabor filters,” EURASIP Journal on Advances in Signal Processing, 95, 2014, pp. 1-17, doi:10.1186/1687-6180-2014-95.

Y. K. Jang, P. J. Kang, and K. R. Park, “A study on eyelid localization considering image focus for iris recognition,” Pattern Recognition Letters, Vol. 29, No. 1, 2008, pp. 1698-1704.

P. Cai, and C. Wang, “An Eyelid Detection Algorithm for the Iris Recognition,” International Journal of Software Engineering and Its Applications, Vol. 9, No. 5, 2015, pp. 105-112. 10.14257/ijseia.2015.9.5.11.

A. AK. Tahir, and S. Anghelus, “A New Method of Eyelid Detection for Iris Recognition,” The XVIII International Conference on Multidisciplinary, "Professor Dorin Paul - Romanian hydropower founder", Cluj, Romania, June (1-2), 2018, in the Romanian Journal of Sceince and Engineering, Revista "ȘTIINȚĂ ȘI INGINERIE", Vol. 33/2018, 171 - 184.

H. Xu, and Z. Ma, “A Practical Design of Gabor Filter Applied to Licence Plate Character Recognition,” International Conference on Computer Science and Information Technology, Singapore, 2008, pp. 397-401, doi: 10.1109/ICCSIT.2008.68.

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’ (Springer Berlin Heidelberg, 2011), 2011, pp. 260–268.

Center for Biometrics and security Research, “Notes on CASIA-IrisV4,” 2010, Available http://www.cbsr.ia.ac.cn/china/Iris%20Databases%20CH.asp


Refbacks

  • There are currently no refbacks.


Abava  Кибербезопасность FRUCT 2024

ISSN: 2307-8162