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Development Of Iris Based Age And Gender Detection System
Abstract
Biometric recognition has shown their utility in different aspect including reducing search space significantly which has led to improve recognition performance, reduced computation time and faster processing time, a person is identified automatically by processing the unique features that are posed by the individual. The existing biometric systems recognize and accept a person, else simply reject a person if not enrolled, but these systems do not give software attributes of a person which is needed to search impostor as it has been observed in many occasions and especially in Nigeria that identification of an individual’s age and gender does not go beyond physical factors which in most cases can be manipulated or spoofed to carry out nefarious activities. This project focuses on developing an iris-based age and gender detection system for certain individuals to identify a person in real time. Left and right irises of 190 subjects constituting 1,716 images were acquired and Five Hundred and Seventy (570) left Irises were normalized to a uniform size of 250 by 250 pixels. Three Hundred and Forty-Two (342) images were used for training while the remaining Two Hundred and Twenty-Eight (228) were used for testing. The acquired images were preprocessed by performing segmentation, filtering and normalization using Daugman’s Rubber Sheet Model. Deep learning pre-trained networks are adopted to extract features from iris images. Further, these features are trained and classified using the multi-class Support Vector Machines (SVM) model for performance evaluation of the system. The system was implemented in Matrix Laboratory 9.0 (R2016a). The performance of the system was evaluated using accuracy, precision, recall and False Positive Rate (FPR). The hypothesis stating ‘the iris has age, and gender-related information’ is proven correct from experimental results. The evaluation results showed that False Positive Rate, Recall, Precision and Accuracy of gender prediction were 2.19%, 92.31%, 96.55% and 95.62% respectively at 0.75 threshold while for age prediction, the best values were obtained at age group of 20-24 for FPR, Recall, Precision and Accuracy which were 5.61%, 94.37%, 96.18% and 94.38% respectively. The developed model gave good performance with high recognition accuracy, recall, precision and low FPR values. Therefore, the developed model can be used in all firms and industries where security and personal identification is desired for security purpose and in going through investigation of criminal records for detecting age and gender of individuals.
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Abbasi A., Khan M., and Khan S., (2013). “A Critical Survey of Iris based Recognition Systems” Middle-East Journal of Scientific Research, 15(5), 663-668.
Abiyev, Rahib H., and Koray Altunkaya, (2008). "Personal iris recognition using neural network." Intl. J. Security & its Appl, 2(2), 41-50.
Adam M., Rossant F., Mikovicova B., and Amiel F., (2009). “Iris identification based on a Local analysis of the iris texture”. Proceedings of 6th International Symposium on Image and Signal Processing and Analysis (ISPA 2009), 523-528.
Al-Qunaieer F. S. and Ghouti L., (2009). “Color Iris Recognition Using Hyper complex Gabor Wavelets”. Symposium on Bio-inspired Learning and Intelligent Systems for Security (BLISS’09), 18-19.
Amit S. and James A. L. (1990) “Federated Database System for Managing Distribution of Heterogeneous and Autonomous Databases” Article in ACM surveys 22 (3): 183-236. AT&T Cambridge Laboratories, “The Database of Faces.” http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
Aworinde, H., and Onifade, O.F.W. (2019). A soft computing model of soft biometric traits for Gender and Ethnicity classification. International Journal of Engineering andManufacturing, 9(2), 54 -63. https://doi.org/10.5815/ijem.2019.02.05
Baig A., Bouridane A., Kurugollu F., and Qu G., (2009). “Fingerprint-iris fusion based Identification system using a single Hamming distance”. Symposium on Bio-inspired Learning and Intelligent Systems for Security (BLISS ’09), 9-12.
Bastys A., Kranauskas J., and Masiulis R., (2009). “Iris Matching by Local Extremum Points of Multiscale Taylor Expansion,” Advances in Biometrics: Lecture Notes in Computer Science #5558, 1070-1079.
Bodade R.M. and Talbar S.N., (2009). “Shift Invariant Iris Feature Extraction Using Rotated Complex Wavelet and Complex Wavelet for Iris Recognition System.” Seventh International Conference on Advances in Pattern Recognition (ICAPR ’09), 449-452.
Boddeti V. N. and Vijaya Kumar B.V.K., (2010). “Extended-Depth-of-Field Iris Recognition Using Unrestored Wavefront-Coded Imagery”. IEEE Transactions on Systems, Man, and Cybernetics -Part A: Systems and Humans 40 (3), 495-508.
Butler A., (2010). “The ACT for Youth Online Presentation: Adolescent Ethnic and Racial Identity Development,” Cornell University.
Campos S., Salas R., Allende H., and Castro C., (2009). “Multimodal Algorithm for Iris Recognition with Local Topological Descriptors”. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: Lecture Notes in Computer Science #5856, 766-773.
Carlson N. R. and Heth C. D., "Sensation," in Carlson, N. R. Carlson, (2009)
Chen W., Huang R., and Hsieh L., (2009). “Iris Recognition Using 3D Co-occurrence Matrix”.Advances in Biometrics: Lecture Notes in Computer Sci #5558, 1122- 1131.
Chen W.-S., Chuan C.-A., Shih S.-W. and Chang S.-H., (2009). “Iris recognition using 2D LDA + 2 DPCA”. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2009), 869-872.
Chowhan S.S. and Shinde G.N., (2009). “Evaluation of Statistical Feature Encoding Techniques on Iris Images.” 2009 WRI World Congress on Computer Science and Information Engineering, vol. 7, 71-75.
Daugman, J. (1993). High confidence visual recognition of person by a test of statistical independence. IEEE Trans. PAMI (15), pp. 1148-1161.
Daugman, J. (1999). “Countermeasures against subterfuge,” in Biometrics: Personal Identification in Networked Society, Jain, Bolle, and Pankanti, Eds.Amsterdam: Kluwer, 1999, pp. 103–121.
Daugman, J. (2004). How iris recognition works. IEEE Trans. Circuits and Syst. For video Tech, 14(2), pp. 21-30.
Daugman, J. and Downing C., (2008). “Effect of Severe Image Compression on Iris Recognition Performance”. IEEE Transactions on Information Forensics and Security 3 (1), 52-61
Demirel, Hasan, and Gholamreza Anbarjafari. (2008). "Iris recognition system using combined colour statistics." In Signal Processing and Information Technology, 2008. ISSPIT 2008. IEEE International Symposium on, pp. 175-179. IEEE.
Dong W., Sun Z. and Tan T., (2009). “A Design of Iris Recognition System at a Distance”. Chinese Conference on Pattern Recognition (CCPR 2009), 1-5.
Dong W., Tan T., and Sun Z., (2010). “Iris Matching Based on Personalized Weight Map”. IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear.
Dozier, G., Frederiksen K., Meeks R., (2009). “Minimizing the number of bits needed for iris recognition via Bit Inconsistency and GRIT”. IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, (CIB 2009), 30-37.
Elmadani A. B., (2010). “Human Authentication Using FingerIris Algorithm Based on Statistical Approach.” Second International Conference on Networked Digital Technologies (NDT 2010), 288-296.
Erbilek M. and Fairhurst M., (2012). "A Methodological Framework for Investigating Age Factors on the Performance of Biometric Systems," in The 14th ACM Workshop on Multimedia and Security, Coventry, UK.
Erbilek M., and Toygar O., (2009). “Recognizing partially occluded irises using subpattern based approaches”. 24th International Symposium on Computer and Information Sciences (ISCIS 2009), 606-610.
Eskandari M. and Toygar O., (2009). “Effect of eyelid and eyelash occlusions on iris images Using Sub-pattern-based approaches”. Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, (ICSCCW 2009), 1-4.
Fairhurst M. and Erbilek M., (2011). "Analysis of physical ageing effects in iris biometrics," Computer Vision, IET, vol. 5, pp. 358-366.
Fatt R. Y., Tay Y. H. and Mok K. M., (2009). “DSP-Based Implementation and Optimization of an Iris Verification Using Textural Feature”. Sixth International Conference on a Fuzzy Systems and Knowledge Discovery (FSKD ’09), vol. 5, 374378.
Fatt R., Haur T.Y. and Mok K. M., (2009). “Iris Verification Algorithm Based on Texture Analysis and its Implementation on DSP”. International Conference on Signal Acquisition and Processing (ICSAP 2009), 198-202.
Fierrez J., Galbally J., Ortega-Garcia J., et al., (2010). “BiosecurID: a multimodal biometric database”. Pattern Analysis and Applications 13, 235-246.
Flom L, Safir A (1987) Iris recognition system, U.S. Patent 4 641 349
Gan J.Y. and Liu J.-F., (2009). “Fusion and recognition of face and iris feature based on Wavelet feature and KFDA”. International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR 2009), July, 47-50.
Gil Santos and Edmundo Hoyle, (2012). "A fusion approach to unconstrained iris recognition", a Pattern Recognition Letters, 33(8), 984-990.
Golomb BA, Lawrence DT, Sejnowski TJ., (1990). Sexnet: A neural network identifies sex from human faces. Advance in Neural Information Processing Systems; 572-577.
Grabowski K., Sankowski W., Zubert M., and Napieralska M., (2009). “Iris structure acquisition method”. 16th International Conference Mixed Design of Integrated Circuits and Systems (MIXDES ’09), 640-643.
Hammerle-Uhl J., Pr¨ahauser C., Starzacher T., and Uhl A., (2009). “Improving Compressed Iris Recognition Accuracy Using JPEG2000 RoI Coding”. Advances in Biometrics: Lecture Notes in Computer Science #5558, 1102-1111.
He Y., Yang H., Hou Y., and He H., (2008). “An Elimination Method of Light Spot Based on Iris Image Fusion”. Communication in Computer and Information Sci 15(12), 415-422.
He Z. and Sun Z. and Tan T. and Qiu X., (2008). “Enhanced Usability of Iris Recognition Via Efficient User Interface and Iris Image Restoration”. 15th IEEE Int. Conf. on Image Processing (ICIP 2008), 261-264.
Hollingsworth K., Bowyer K., and Flynn P., (2009). “Image Averaging for Improved Iris Recognition”. Advances in Biometrics: Lecture Notes in Computer Science #5558, 1112-1121.
Hollingsworth K., Peters T., Bowyer K.W. and Flynn P.J., (2009). “Iris Recognition Using Signal- Level Fusion of Frames From Video”. IEEE Transactions on Information Forensics and Security 4 (4), 837 -848.
Hollingsworth K.P., Bowyer K.W. and Flynn P.J., (2009). “The Best Bits in an Iris Code”. IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (6), 964-973.
Individual Biometric: iris scan. (2002). Retrieved November 22, 2007, from National Center for State Courts: http//ctl.ncsc.dni.us
Irsch, K., & Guyton, D. (2009). Anatomy of Eyes. In Encyclopedia of Biometrics (pp.11-16). New York: Springer.
Johnson P.A., Lopez-Meyer P., Sazonova N., Hua F. and Schuckers S., (2010). “Quality in face and Iris research ensemble (Q-FIRE)”. Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS 10).
Johnson RG (1991) Can iris patterns be used to identify people. Los Alamos National Laboratory, CA, Chemical and Laser Sciences Division, Rep. LA-12331-PR
Kalka N., Bartlow N. and Cukic B., (2009). “An automated method for predicting iris Segmentation failures”. IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems (BTAS 09), 28-30, 1 -8.
Kalka N., Zuo J., Schmid N. A. and Cukic B., (2010). “Estimating and Fusing Quality Factors for Iris Biometrics Images”. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 40 (3), 509-524.
Kamal Hajari, Ujwalla Gawande, and Yogesh Golhar, (2016). "Neural Network Approach to Iris Recognition in Noisy Environment", Procedia Computer, Vol. 78, pp. 675-682.
Kannavara R. and Bourbakis N., (2009). “Iris biometric authentication based on local global graphs: An FPGA implementation”. IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA 2009), 1-7.
Kronfeld, P. (1962). Gross anatomy and embryology of the eye. In H. Davson, & E. London, in The Eye. UK: Academic.
Kyaw K. S. S., (2009). “Iris Recognition System Using Statistical Features for Biometric”.
International Conference on Electronic Computer Technology, 554 -556.
Labati R.D., Piuri V. and Scotti F., (2009). “Neural-based iterative approach for iris detection in iris recognition systems”. IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA 2009), 1-6.
Lagree S. and Bowyer K., (2011). “Predicting Ethnicity and Gender from Iris Texture,” in Proceedings of IEEE International Conference on Technologies for Homeland Security, Waltham, pp. 440-445.
Latinwo, B. M., Falohun, A.S., Omidiora, E.O., and Makinde, B.O. (2018). Iris Texture Analysis for Ethnicity Classification Using Self-Organizing Feature Maps. Journal of a Advancesin Mathematics and Computer Science25(6), 1-10.
Lee Y., Phillips P., and Micheals R., (2009) “An Automated Video-Based System for Iris Recognition”. Advances in Biometrics: Lecture Notes #5558,1160-1169.
Li LU, Ziyi XU, Pengfei SHI. (2009). Gender classification of facial images based on multiple facial regions. World Congress on Computer Science and Information Engineering : 48-52.
Lin J., Li J.-P., Lin H. and Ming J., (2009). “Robust person identification with face and iris by modified PUM method”. International Conference on Apperceiving Computing and Intelligence Analysis (ICACIA 2009), 321 -324.
Liu L., Gu X.-F., Li J.-P., Lin J., Shi J.-X. and Huang Y.-Y., (2009). “Research on data fusion of multiple biometric features”. International Conference on Apperceiving Computing and Intelligence Analysis (ICACIA 2009), 23-25 Oct., 112 -115.
Matey J.R. and Kennell L.R., (2009). “Iris Recognition -Beyond One Meter, Handbook of`Remote Biometrics.
McCloskey S., Wing Au A. and Jelinek J., (2010). “Iris capture from moving subjects using a Fluttering shutter”. Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS 10).
Mehrotra H., Badrinath G.S., Majhi B. and Gupta P., (2009). “An efficient dual stage approach for iris feature extraction using interest point pairing”. IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications (CIB 2009), 59-62.
Miyazawa K., Ito K., Aoki T. and Kobayashi K., (2008). “An Effective Approach for Iris Recognition Using Phase-Based Image Matching”. IEEE Transactions on Pattern Analysis and Machine Intelligence 30 (10), Oct., 1741-1756.
Moravec P., Gajdos P., Snasel V., and Saeed K., (2009). “Normalization Impact on SVD` Based Iris Recognition”. International Conference on Biometrics and Kansei Engineering (ICBAKE2009), June, 60-64.
Muron, A., & Pospisil, J. (2000). The human iris structure and its usages. Acta Univ. Palacki. Olomuc. Fac. Rerum Nat. Phys., pp. 87–95.
Nandakumar K, Jain AK, Nagar A (2008) Biometric template security. EURASIP J Adv Signal Process 2008(13):113
Newton E.M. and Phillips P.J., (2009). Meta-Analysis of Third-Party Evaluations of Iris Recognition. IEEE Transaction on Systems, Man and Cybernetics part A 39(1), 4-11.
Patil C. M. and Patilkulkarani S., (2009). “Iris Feature Extraction for Personal Identification Using Lifting Wavelet Transform”. International Conference on Advances in Computing, Control, and Telecommunication Technologies (ACT ’09), 764-766.
Phillips P., Flynn P., Beveridge J., Scruggs W., OToole A., Bolme D., Bowyer K., Draper B., Givens G., Lui Y., Sahibzada H., Scallan J., and Weimer S., (2009). “Overview of the Multiple Biometrics Grand Challenge, Advances in Biometrics: Lecture Notes in Computer Science #5558.
Phillips P.J., Scruggs W., O’Toole A., Flynn P., Bowyer K.W., Schott C. and Sharpe M., (2010). “FRVT2006 and ICE 2006 Large-Scale Experimental Results”. IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (5), 831-846.
Poornima S., Rajavelu C., and Subramanian S., (2010). "Comparison and a neural network approach for iris localization", Procedia Computer Science Vol. 2, pp. 127-132.
Proenca H., (2008). “Iris Recognition: A Method to Segment Visible Wavelength Iris Images Acquired On-the-Move and At-a-Distance”. Advances in Visual Computing: Lecture Notes in Computer Science #5358 32 (8), 731-742.
Proenca H., (2009). “On the feasibility of the visible wavelength, at-a-distance and on-the move iris recognition”. IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications (CIB 2009), 9-15.
Proenca H., (2010). “Iris Recognition: On the Segmentation of Degraded Images Acquired in the Visible Wavelength”. IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (8), 1502-1516.
Proenca H., (2010). “Quality Assessment of Degraded Iris Images Acquired in the Visible Wavelength” IEEE Transactions on Information Forensics and Security, to appear.
Proenca H., Filipe S., Santos R., Oliveira J. and Alexandre L., (2010). “The UBIRIS.v2: A Database of Visible Wavelength Images Captured On-The-Move and At-A-Distance”. IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (8),1529-1535.
Qiu X., Sun Z., and Tan T., (2005). "Global Texture Analysis of Iris Images for Ethnic Classification” in Advances in Biometrics. vol. 3832, D. Zhang and A. Jain, Eds., ed: Springer Berlin / Heidelberg, , pp. 411-418.
Ren J. and Xie M., (2009). “Evaluation of Iris Images Definition Based on Pupil’s Edge Kurtosis.” 2nd International Congress on Image and Signal Processing, 2009 (CISP’09), 1-4.
Ren J. and Xie M., (2009). “Research on Clarity-Evaluation-Method for Iris Images”. Second International Conference on Intelligent Computation Technology and Automation (ICICTA ’09), vol. 1, 682-685.
Ricanek K., (2009). “Dissecting the human identity”. Computer, 96-97.
Rose, A., (2010). IRIS Recognition: The Path Forward. 2(6): 1492-1499. IEEE Computer Society
Ross A., Pasula R. and Hornak L., (2009) “Exploring multispectral iris recognition beyond 900nm”. IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems (BTAS09).
Roy K. and Bhattacharya P., (2009). “Level Set Approaches and Adaptive Asymmetrical SVMs Applied for Non ideal Iris Recognition,” Image Analysis and Recognition: Lecture Notes in Computer Science #5627, 418-428.
Roy K. and Bhattacharya P., (2009). “Iris Recognition in Nonideal Situations,” Information Security: Lecture Notes in Computer Science #5735, 143-150.
Roy K. and Bhattacharya P., (2009). “Nonideal Iris Recognition Using Level Set Approach and Coalitional Game Theory,” Computer Vision Systems: Lecture Notes in Computer Science #5815, 394-402.
Sanderson, S. and J. Erbetta. (2000). “Authentication for Secure Environments based on Iris Scanning Technology”. IEE Colloquium on Visual Biometrics.
Sgroi, A., K. Baker, W. Bowyer and P. Flynn, (2013). “The Prediction of Old and Young Subjects from Iris Texture”, In: IAPR International Conference on Biometrics.
Shirke Swati D., Shirke Suvarna D., Gupta, (2012). IRIS Recognition, Emerging Trends in Computer Science and Information Technology (ETCSIT2012) Proceedings published in International Journal of Computer Applications (IJCA).
Song, Yun, Wei Cao, and Zunliang He. (2014) "Robust iris recognition using sparse error correction model and discriminative dictionary learning." Neurocomputing137 (2014): 198-204.
Sudha Gupta, (2010). IRIS Recognition System using Biometric Template Matching Technology Asst. Professor, LMIETE, LMISTE,Viral Doshi, Abhinav Jain and Sreeram Iyer, K.J.S.C.E. Mumbai India International Journal of Computer Applications (09758887), 1(2).
Sudha N., Puhan N.B., Xia H. and Jiang X., (2009). “Iris recognition on edge maps”. IET Computer Vision 3 (1), 1-7.
Swathi S. Dhage, Sushma Shridhar Hegde, K. Manikantan and S. Ramachandran, (2015). "DWT-based feature extraction and radon transform based contrast enhancement for improved iris recognition", Procedia Computer Science Vol. 45, pp. 256-265,.
Tajbakhsh N., Misaghian K., and Bandari N., (2009). “A Region-Based Iris Feature Extraction Method Based on 2D-Wavelet Transform, Biometric ID Management and Multimodal Communication: Lecture Notes in Computer Science #5707, 301-307.
Thompson J.W. and Flynn P.J., (2010). “A segmentation perturbation method for improved iris recognition”. Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS 10).
Tieniu Tan, Xiaobo Zhang, Zhenan Sun and Hui Zhang, (2012). "Noisy iris image matching by using multiple cues", Pattern Recognition Letters 33(8), 970-977.
Vatsa M., Singh R., Noore A. and Singh S.K., (2009). “Belief Function Theory Based Biometric Match Score Fusion: Case Studies in Multi-instance and Multi-Unit Iris Verification”. Seventh International Conference on Advances in Pattern Recognition (ICAPR ’09), 433.
Velisavljevic V., (2009). “Low-Complexity Iris Coding and Recognition Based on Direction lets”. IEEE Transactions on Information Forensics and Security 4 (3), 410-417.
Wang J., Li Y., Ao X., Wang C. and Zhou J., (2009). “Multi-modal biometric authentication Fusing iris and palmprint based on GMM”. IEEE 15th Workshop on Statistical Signal Processing (SSP ’09), 349-352.
Wang Q., Zhang X., Li M., Dong X., Zhou Q., and Yin Y., (2012). “Adaboost and multi orientation 2D Gabor-based noisy iris recognition,” Pattern Recognition. Lett. 33(8), 978–983S.
Wang Z., Li Q., Niu X. and Busch C., (2009). “Multimodal Biometric Recognition Based on Complex KFDA”. Fifth International Conference on Information Assurance and Security (IAS’09), vol. 2, 177 -180.
Wang Z.F., Han Q., Li Q., Niu X.M. and Busch C., (2009). “Complex common vector for Multimodal biometric recognition”. Electronics Letters 45 (10), 495-496.
Wen-Sheng Chu, Chun-Rong Huang, Chu- Song Chen, (2010). Identifying gender from unaligned facial images by set classification. In Proc. IEEE 20thInternational Conference on Pattern Recognition; 2636–2639.
Wheeler F. W., Amitha Perera A. G., Abramovich G., Yu B. and Tu P. H., (2008). “Stand-off Iris Recognition System”. IEEE 2nd International Conference on Biometrics: Theory, Applications, and Systems (BTAS 08).
Wibowo E.P. and Maulana W.S., (2009). “Real-Time Iris Recognition System Using a Proposed Method”. International Conference on Signal Processing Systems, 15-17, 98-102.
Wildes RP (1997) Iris recognition: an emerging biometric technology. Proc. IEEE85(9):1348- 1363
Wildes et al. “Automated, non-invasive iris recognition system and method”. U.S. Patent No. 5,572,596 and 5,751,836.
Xu X. and Guo P., (2009). “Iris Feature Extraction Based on the Complete 2 DPCA,” Advances in Neural Networks: Lecture Notes in Computer Science #5552, 950-958.
Xu. X., (2013). “A New local and Global Model to Iris Recognition”, Advanced Materials Research, pp: 658.
Zhenan Sun; Hui Zhang; Tieniu Tan; Jianyu Wang, (2014). "Iris Image Classification Based on Hierarchical Visual Codebook," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.36, no.6, pp.1120,1133.Zhou, Steve, and Junping Sun. (2013). "A novel approach for code match in iris recognition." In Computer and Information Science (ICIS), 2013 IEEE/ACIS 12th International Conference on, pp. 123-128. IEEE.
Zuo J., Nicolo F. and Schmid N.A., (2010). “Cross spectral iris matching based on predictive image mapping”. Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS 10).
DOI: http://dx.doi.org/10.52155/ijpsat.v34.1.4477
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