An Extensive Study of Issues, Challenges and Achievements in Iris Recognition

Authors

  • Sunil Swamilingappa Harakannanavar Department of Electronics and Communication Engineering, S. G. Balekundri Institute of Technology, Belagavi, Karnataka, India
  • C. R. Prashanth Department of Telecommunication Engineering, Dr. Ambedkar Institute of Technology, Bangalore, Karnataka, India
  • Vidyashree Kanabur Department of Electronics and Communication Engineering, S. G. Balekundri Institute of Technology, Belagavi, Karnataka, India
  • Veena I. Puranikmath Department of Electronics and Communication Engineering, S. G. Balekundri Institute of Technology, Belagavi, Karnataka, India
  • K. B. Raja Department of Electronics and Communication Engineering, University of Visvesvaraya College of Engineering, Bangalore, Karnataka, India

DOI:

https://doi.org/10.51983/ajes-2019.8.1.2336

Keywords:

Iris Segmentation, Feature Extraction, Performance Metrics, Acquisition, Normalization

Abstract

In recent years biometric identification of persons has gained major importance in the world from its applications, such as border security, access control and forensic. Iris recognition is one of the most booming biometric modalities. Due to its unique character as a biometric feature, iris identification and verification systems have become one of the most accurate biometric modality. In this paper, the different steps to recognize an iris image which includes acquisition, segmentation, normalization, feature extraction and matching are discussed. The performance of the iris recognition system depends on segmentation and normalization techniques adopted before extracting the iris features. It also provides an extensive review of the significant methods of iris recognition systems. In addition to this, the challenges and achievements of the iris are presented.

References

J. Daugman, "How Iris Recognition Works," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21-30, 2004.

R. P. Wildes, "Iris Recognition: An Emerging Biometric Technology," IEEE Proceedings, vol. 85, no. 9, pp. 1348-1363, 1997.

H. Proenca and L. Alexandre, "Iris segmentation methodology for non-cooperative recognition," IEEE Proceedings Vision, Image and Signal Processing, vol. 153, no. 2, pp. 199-205, 2006.

L. Ma and T. Tan, "Efficient Iris Recognition by Characterizing Key Local Variations," IEEE Transactions on Image Processing, vol. 13, no. 6, pp. 739-750, 2004.

L. Ma, T. Wang, and T. Tan, "Iris recognition based on multichannel Gabor filtering," in Asian Conference on Computer Vision, pp. 279-283, 2002.

Camus and Wildes, "Reliable and fast eye finding in close-up images," in IEEE Proceedings of International Conference on Pattern Recognition, pp. 389-394, 2002.

J. Daugman, "New methods in iris recognition," IEEE Transactions on Systems, Man and Cybernetics, vol. 37, no. 5, pp. 1167–1175, 2007.

W. K. Kong and D. Zhang, "Detecting eyelash and reflection for accurate iris segmentation," International Journal of Pattern Recognition, vol. 17, no. 6, pp. 1025-1034, 2003.

Y. Chen, S. Dass, and A. K. Jain, "Localized iris image quality using 2-D wavelets," in IEEE International Conference, pp. 373-381, 2006.

J. Daugman, "Probing the uniqueness and randomness of Iris Codes: Results from 200 billion iris pair comparisons," IEEE Transactions, vol. 94, no. 11, pp. 1927-1935, 2006.

M. Nabti and A. Bouridane, "An effective and fast iris recognition system based on a combined multiscale feature extraction technique," International Journal on Pattern Recognition, vol. 41, no. 3, pp. 868-879, 2008.

X. He and P. Shi, "A new segmentation approach for iris recognition based on hand-held capture device," International Journal on Pattern Recognition, vol. 40, no. 4, pp. 1326-1333, 2007.

L. Yu, D. Zhang, and K. Wang, "The relative distance of key point based iris recognition," Pattern Recognition, vol. 40, no. 2, pp. 323-430, 2007.

K. Nandakumar, Y. Chen, S. C. Dass, and A. K. Jain, "Likelihood Ratio Based Biometric Score Fusion," IEEE Transactions on Pattern Analytics and Machine Intelligence, vol. 30, pp. 342-347, 2008.

J. Thornton, M. Savvides, and B. V. Kumar, "A Bayesian approach to deformed pattern matching of iris images," IEEE Pattern Analytics and Machine Intelligence, vol. 29, pp. 596-606, 2007.

J. Daugman, "High confidence visaual recognition of persons by a test of statistical independence," IEEE Pattern Analytics and Machine Intelligence, vol. 15, pp. 1148-1161, 1993.

L. Yu, D. Zhang, K. Wang, and W. Yang, "Coarse iris classification using box-counting to estimate fractal dimensions," International Journal on Pattern Recognition, vol. 38, no. 11, pp. 1791-1798, 2005.

C. Sanchez-Avila and R. Sanchez-Reillo, "Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation," International Journal on Pattern Recognition, vol. 38, no. 2, pp. 231-240, 2005.

W. W. Boles, "A security system based on human iris identification using wavelet transform," in International Conference on Knowledge-Based Intelligent Electronic Systems, pp. 533-541, 1997.

W. W. Boles and B. Boashash, "A human identification technique using images of the iris and wavelet transform," IEEE Transactions on Signal Processing, vol. 46, no. 4, pp. 1185–1188, 1998.

S. S. Harakannanavar and V. I. Puranikmath, "Comparative Survey of Iris Recognition," in IEEE International Conference on Electrical, Electronics, Communication, Computer and Optimization techniques, pp. 280-283, 2017.

S. S. Harakannanavar et al., "IREMD: An Efficient Algorithm for Iris Recognition," International Journal of Advanced Networking and Applications, vol. 9, no. 5, pp. 3580-3587, 2018.

R. Dillak and M. Bintiri, "A Novel Approach for Iris Recognition," in IEEE International Symposium, pp. 231-236, 2016.

M. Sharkas, "Neural Network based approach for Iris Recognition based on both eyes," in IEEE International conference on SAI Computing, pp. 253-258, 2016.

A. Mozumder and S. Begum, "An Efficient Approach towards Iris Recognition with modular neural network match scores Fusion," in IEEE International conference on Computational Intelligence and Computing Research, pp. 1-6, 2016.

M. Rizk, H. Farag, and L. Said, "Neural Network Classification for Iris Recognition using both particle swarm Optimization and Gravitational Search Algorithm," in IEEE International conference on World Symposium on Computer Applications and Research, pp. 12-17, 2016.

H. Naderi et al., "Fusing Iris, Palm print and Finger print in a Multi-Biometric Recognition system," in IEEE International Conference on Computer and Robot Vision, pp. 327-334, 2016.

A. Sallehuddin et al., "Score Level Normalization and Fusion of Iris Recognition," in International Conference on Electronic Design, pp. 464-469, 2016.

Rangaswamy and R. K. B, "Straight-line Fusion based IRIS Recognition using AHE, HE and DWT," in Elsevier International Conference on Advanced Communication Control and Computing Technologies, pp. 228-232, 2016.

S. Minaee et al., "An Experimental study of Deep Convolution Features for Iris Recognition," in International Conference on Signal Processing Medicine and Biology Symposium, pp. 1-6, 2016.

Charan, "Iris Recognition using Feature Optimization," in Elsevier International conference on Applied and Theoretical Computing and Communication Technology, pp. 726-731, 2016.

N. Rao P, M. Hebbar, and K. Manikantan, "Feature Selection using dynamic binary Particle Swarm Optimization for Iris Recognition," in International Conference on Signal Processing and Integrated Networks, pp. 139-146, 2016.

K. Raja, R. Ragahavendra, and C. Busch, "Scale-level Score Fusion of Steered Pyramid Features for Cross-Spectral Periocular Verification," in International conference on Information Fusion, pp. 1-5, 2017.

K. Devi, P. Gupta, D. Grover, and A. Dhindsa, "An Effective Texture Feature Extraction Approach for Iris Recognition System," in International Conference on Advances in Computing, Communication, and Automation, pp. 1-5, 2016.

S. Emerich, R. Malutan, E. Lupu, and L. Lefkovits, "Patch Based Descriptors for Iris Recognition," in International Conference on Intelligent Computer Communication and Processing, pp. 187-191, 2016.

N. Suciati et al., "Feature Extraction Using Statistical Moments of Wavelet Transform for Iris Recognition," in IEEE International conference on information and communication technology and systems, pp. 193-198, 2016.

U. Gawande, K. Hajari, and Y. Golhar, "Novel Technique For Removing Corneal Reflection in Noisy Environment-Enhancing Iris Recognition Performance," in IEEE International conference on signal and information processing, pp. 1-5, 2016.

R. Vyas, T. Kanumuri, and G. Sheoran, "Iris Recognition Using 2-D Gabor filter and XOR-SUM Code," in IEEE International conference on information processing, pp. 1-5, 2016.

S. Salve and S. Narote, "Iris Recognition Using SVM and ANN," in International Conference on Wireless Communications, Signal Processing and Networking, pp. 474-478, 2016.

D. Kumar, M. Sastry, and K. Manikkantan, "Iris Recognition using Contrast Enhancement and Spectrum-Based Feature Extraction," in IEEE International conference on Emerging trends in Engineering, Technology and Science, pp. 1-7, 2016.

S. Sheela and Abhinand, "Iris Detection for Gaze Tracking Using Video Frames," in IEEE International Conference on Advance Computing, pp. 629-633, 2015.

A. Satish, Adhau, and D. Shedge, "Iris Recognition methods of a blinked Eye in Non-ideal Condition," in IEEE International Conference on Information Processing, pp. 75-79, 2016.

C. Tan and Ajaykumar, "Accurate Iris Recognition at a Distance Using Stabilized Iris Encoding and Zernike Moments Phase Features," IEEE Transactions on Image Processing, vol. 23, no. 9, pp. 3962-3974, 2014.

Kavita and S. Agrawal, "An Iris Recognition Based on Robust Intrusion Detection," in IEEE Annual India Conference, pp. 1-6, 2016.

K. Popplewell et al., "Multispectral Iris Recognition Utilizing Hough Transform and Modified LBP," in IEEE International Conference on Systems, Man, and Cybernetics, pp. 1396-1399, 2014.

A. J. S et al., "Iris Recognition using Hybrid Domain Features," in Annual IEEE India Conference, pp. 1-5, 2015.

A. Gale and S. Salankar, "Evolution of performance Analysis of Iris Recognition System By using Hybrid method of Feature Extraction and matching by Hybrid Classifier for Iris Recognition system," in IEEE International Conference on Electrical, Electronics and Optimization Techniques, pp. 3259-3263, 2016.

K. Nguyen et al., "Iris Recognition with Off-the-Shelf CNN Features a Deep Learning Perspective," IEEE Article, no. 99, pp. 1-1, 2017.

M. Baqar et al., "Deep Belief Networks for Iris Recognition based on Contour Detection," in IEEE International Conference on Open source systems and technologies, pp. 72-77, 2016.

S. Alkassar et al., "Robust Sclera Recognition System with Novel Sclera Segmentation and Validation Techniques," IEEE Transactions on Systems, Man, and Cybernetics Systems, pp. 474-486, 2017.

Z. Li, "An Iris Recognition Algorithm Based on Coarse and Fine Location," in IEEE International Conference on Big Data Analysis, pp. 744-747, 2017.

L. Su et al., "Iris Location Based on Regional Property and Iterative Searching," in IEEE International Conference on mechatronics and Automation, pp. 1064-1068, 2017.

X. Tong et al., "An Eye State Recognition Algorithm Based on Feature Level Fusion," in IEEE International Conference on Vehicular Electronics and Safety, pp. 151-155, 2017.

K. Kumar and M. Pavani, "LBP Based Biometric Identification using the Periocular Region," in IEEE Annual Information Technology Electronics and mobile Communication Conference, pp. 204-209, 2017.

Downloads

Published

22-02-2019

How to Cite

Swamilingappa Harakannanavar, S., Prashanth, C. R., Kanabur, V., Puranikmath, V. I. ., & K. B. Raja. (2019). An Extensive Study of Issues, Challenges and Achievements in Iris Recognition. Asian Journal of Electrical Sciences, 8(1), 25–35. https://doi.org/10.51983/ajes-2019.8.1.2336