Iris as a biometric trait has established itself in last two decades. Iris being unique and reliable is used for recognition and authentication purpose instead of using Passwords and PINs. Security is a major concern in any recognition system so is the case with iris recognition system. Further, recognition performance depends upon many factors among which distance, lighting conditions, subject cooperation, and pupil dynamics to name some important ones. The above mentioned covariates have been studies vastly in reference to iris recognition. In this paper, we have considered various novel factors that affect the performance of iris recognition system. Pupil dilation, contact lenses, periocular recognition, template aging, use of drugs and alcohol and sensor interoperability have been under investigation as emerging covariates of iris recognition; in recent times. The focus of this paper is to present a review of various covariates (existing as well as emerging) and their effects on recognition performance. This work shows that these covariates have considerable effect on iris recognition performance and need to be considered while implementing any commercial iris recognition systems.

Covariates, Iris Recognition, Pupil dilation, Contact Lenses, Synthetic Iris, Template Aging, Interoperability

[1] P. Meredith, T. Sarna, “The Physical and Chemical Properties of Eumelanin,” Pigment Cell Research, Vol. 19, pp. 572–594, 2006.
[2] UIDAI Dashboard [Accessed 21st May 2019] https://uidai.gov.in/aadhaar_dashboard/
[3] Z. Sun and T. Tan, “Ordinal Measures for Iris Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 12, pp. 2211–2226, Aug. 2009.
[4] L. Flom and A. Safir, ‘‘Iris Recognition System,’’ U.S. Patent 4 641 349, Feb. 3, 1987.
[5] S. Chawla and A. Oberoi, “A Robust Algorithm for Iris Segmentation and Normalization using Hough Transform,” 5th IEEE Int. Conf. on Advanced Computing & Communication Technologies, pp. 457-460, Nov. 2011.
[6] Richard P. Wildes, “Iris Recognition: An Emerging Biometric Technology,” Proceedings of the IEEE, Vol. 85, No. 9, September 1997.
[7] J. Daugman, “New Methods in Iris Recognition,” IEEE Trans. Syst. Man Cybern. 37(5), 1167–1175 (2007).
[8] H. P. Proenca, “Towards Non-Cooperative Biometric Iris Recognition,” Ph.D. Dissertation, Dept. Comput. Sci., Univ. Beira Interior, Covilhã, Portugal, 2006.
[9] S. P. Fenker and K. W. Bowyer, “Experimental Evidence of a Template Aging Effect in Iris Biometrics,” in Proc. IEEE Comput. Soc. Workshop Appl. Comput. Vis., Jan. 2011, pp. 232–239.
[10] S. Baker, K. Bowyer, P. Flynn, “Empirical Evidence for Correct Iris Match Score Degradation with Increased Time-Lapse between Gallery and Probe Matches”, in Proceedings of the Third International Conference on Biometrics, 2009, pp. 1170–1179.
[11] K. W. Bowyer, S. E. Baker, A. Hentz et al. IDIS 2: 327, 2009.
[12] L. Masek. Recognition of human iris patterns for biometric identification. Bachelors Thesis http://www.csse.uwa.edu.au/˜pk/studentprojects/libor/, The University of Western Australia, 2003.
[13] A. Sgroi, K. W. Bowyer, and P. J. Flynn. “The Impact of Diffuse Illumination on Iris Recognition”, 2013 International Conference on Biometrics (ICB), 2013.
[14] J. G. Daugman, “How Iris Recognition Works,” IEEE Trans. Circuits Syst. Video Technology, vol. 14, no. 1, pp. 21-30, Jan. 2004.
[15] J. Zuo, N. A. Schmid, and X. Chen, “On Generation and Analysis of Synthetic Iris Images,” IEEE Transactions on Information Forensics and Security, 2(1) :77–90, 2007.
[16] S. Makthal and A. Ross, “Synthesis of Iris Images Using Markov Random Fields,” in Proc. of the 13th European Signal Processing Conference (EUSIPCO 05), Antalya, Turkey, Sept. 2005.
[17] F. J. Shareef, S. Sun, M. Kotecha, I. Kassem, D. Azar, M. Cho, “Engineering a Light-Attenuating Artificial Iris,” Invest Ophthalmol Vis Sci. 2016; 57 : 2195–2202. DOI:10.1167/iovs.15-17310.
[18] P. Tome-Gonzalez, F. Alonso-Fernandez, and J. Ortega-Garcia, “On the Effects of Time Variability in Iris Recognition,” in Proc. IEEE Int. Conf. Biometr. Theory, Appl. Syst., Oct. 2008, pp. 1–6.
[19] J. Cui, Y. Wang, J. Huang, T. Tan, and Z. Sun, “An Iris Image Synthesis Method Based on PCA and Super-Resolution,” in Proc. of the 17th Intern. Conf. on Pattern Recognition (ICPR 04), Cambridge, UK, vol. 4, pp. 471-474, 2004.
[20] H. P. Proenca, “Quality Assessment of Degraded Iris Images Acquired in the Visible Wavelength" IEEE Transactions on Information Forensics and Security, pp. 82–95, 2011.
[21] 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, Issue 4, pp. 1185-1188, Apr. 1998.
[22] S. Shah and A. Ross, “Generating Synthetic Irises by Feature Agglomeration,” In IEEE International Conference on Image Processing, pp. 317–320, 2006.
[23] Z. He, T. Tan, Z. Sun, and X. Qiu, “Toward Accurate and Fast Iris Segmentation for Iris Biometrics,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(9), 1670–1684, 2009.
[24] J. Galbally, S. Marcel and J. Fierrez, “Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition,” in IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 710-724, Feb. 2014.
[25] L. Ma, T. Tan, Y. Wang, and D. Zhang, “Efficient Iris Recognition by Characterizing Key Local Variations,” IEEE Transactions on Image Processing, 13(6), 739– 750, 2004.
[26] D. Monro, S. Rakshit, and D. Zhang, “DCT-based Iris Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4), 586–595, 2007.
[27] O. Seyeddain, H. Kraker, A. Redlberger, A. K. Dexl, G. Grabner, M. Emesz, “Reliability of automatic biometric IRIS recognition after phacoemulsification or drug-induced pupil dilation,” European Journal of Ophthalmology, vol. 24, no. 1, pp. 58-62, 2014.
[28] E. H. Hess and J. M. Polt, “Pupil Size in relation to Mental Activity during Simple Problem-Solving,” Science, 143(3611):1190–1192, 1964.
[29] S. S. Arora, M. Vatsa, R. Singh and A. Jain, “Iris recognition under alcohol influence: A preliminary study,” 2012 5th IAPR International Conference on Biometrics (ICB), New Delhi, 2012, pp. 336-341.
[30] J. Peragallo, V. Biousse, N.J. Newman, “Ocular manifestations of drug and alcohol abuse,” Current Opinion in Ophthalmology, Vol. 24(6), pp. 566-73, Nov. 2013, doi:10.1097/ICU.0b013e3283654db2.
[31] I. Tomeo-Reyes, A. Ross and V. Chandran, “Investigating the impact of drug induced pupil dilation on automated iris recognition,” 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), Niagara Falls, NY, 2016, pp. 1-8.doi: 10.1109/BTAS.2016.7791178
[32] P. Artal, “Aging Effects on the Optics of the Eye,” in Age-Related Changes of the Human Eye (Springer, 2008), pp. 35–44.
[33] S. Baker et al., Degradation of iris recognition performance due to non-cosmetic prescription contact lenses. Comput. Vis. Image Underst. 114(9), 1030–1044, 2010.
[34] N. Kohli, D. Yadav, M. Vatsa and R. Singh, “Revisiting iris recognition with color cosmetic contact lenses,” 2013 International Conference on Biometrics (ICB), Madrid, 2013, pp. 1-7.
[35] N. D. Kalka, J. Zuo, N. A. Schmid, and B. Cukic. Image quality assessment for iris biometric. In Defense and Security Symposium, pages 62020D–62020D. International Society for Optics and Photonics, 2006.
[36] B. Winn et al., Factors affecting light-adapted pupil size in normal human subjects. Investig. Ophthalmol. Visual Sci. 35(3), 1132–1137, 1994.
[37] D. Yadav, N. Kohli, J. S. Doyle, R. Singh, M. Vatsa, and K. W. Bowyer, “Unraveling the effect of textured contact lenses on iris recognition,” IEEE Transactions on Information Forensics and Security, 9(5):851–862, 2014.
[38] D.L. Woodard, S. Pundlik, P. Miller, R. Jillela, and A. Ross. On the fusion of periocular and iris biometrics in non-ideal imagery. In 20th International Conference on Pattern Recognition (ICPR), pp. 201–204, Aug. 2010.
[39] K.P. Hollingsworth, S.S. Darnell, P.E. Miller, D.L. Woodard, E. Ortiz, and K.W. Bowyer. Human and machine performance on periocular biometrics under near-infrared light and visible light. Information Forensics and Security, IEEE Transactions on, 7(2):588–601, April 2012.
[40] G. Santos, E. Grancho, M. V. Bernardo, P. T. Fiadeiro, Fusing iris and periocular information for cross-sensor recognition, Pattern Recognition Letters (2014), doi:10.1016/j.patrec.2014.09.012
[41] A. Sharma, S. Verma, M. Vatsa and R. Singh, “On cross spectral periocular recognition,” 2014 IEEE International Conference on Image Processing (ICIP), Paris, 2014, pp. 5007-5011.
[42] U. Park, A. Ross and A. K. Jain, “Periocular biometrics in the visible spectrum: A feasibility study,” 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, Washington, DC, 2009, pp. 1-6. doi: 10.1109/BTAS.2009.5339068
[43] K. W. Bowyer, K. Hollingsworth, P. J. Flynn, “Image understanding for iris biometrics: A survey”, Computer Vision and Image Understanding, 110(2):281–307, May 2008.
[44] A. K. Jain, A. Ross, and K. Nandakumar. Introduction to Biometrics, Chapter Iris Recognition, pp. 141–147. Springer Link, 2011.
[45] K. P. Hollingsworth, K.W. Bowyer, P.J. Flynn, “Pupil dilation degrades iris biometric performance,” Comput. Vis. Image Underst. (2009), Vol. 113, No. 1, pp. 150-157.
[46] C. N. Padole and H. Proenca, “Periocular recognition: Analysis of performance degradation factors,” 2012 5th IAPR International Conference on Biometrics (ICB), New Delhi, 2012, pp. 439-445.
[47] K. B. Raja, R. Raghavendra, V. K. Vemuri, C. Busch, Smartphone based visible iris recognition using deep sparse filtering, Pattern Recognition Letters, Vol. 57, 2015, pp. 33-42.
[48] A. Ross, R. Pasula and L. Hornak, “Exploring multispectral iris recognition beyond 900nm,” 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, Washington, DC, 2009, pp. 1-8.
[49] H. Proenca, “Iris Recognition: On the Segmentation of Degraded Images Acquired in the Visible Wavelength,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 8, pp. 1502-1516, Aug. 2010.
[50] S. Kumar, V. K. Lamba, S. Jangra, “Synthetic Iris as a vulnerability of iris recognition,”, Journal of Image Processing & Pattern Recognition Progress, Vol. 5, No. 3, pp. 15-21, 2018.
[51] O. Bergamin, M.B. Zimmerman, R. H. Kardon, “Pupil light reflex in normal and diseased eyes: Diagnosis of visual dysfunction using waveform partitioning,” Ophthalmology, Vol. 110, No. 1, pp. 106-114, 2003.
[52] M. Fairhurst, M. Erbilek, “Analysis of Physical Ageing Effects in Iris Biometrics”, IET Computer Vision, 5(6), pp. 358–366, 2011.
[53] M. Pedrotti, M. A. Mirzaei, A. Tedesco, J. Chardonnet, F. Merienne, S. Benedetto, and T. Baccino, “Automatic Stress Classification With Pupil Diameter Analysis,” International Journal of Human–Computer Interaction, 30(3), 220-236, 2014.
[54] Z. Sun and T. Tan, "Ordinal Measures for Iris Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 12, pp. 2211-2226, Dec. 2009.
[55] S. Baker, K. W. Bowyer, P.J. Flynn, “Contact lenses: handle with care for iris recognition,” Proceedings of The Third Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS 09), September 2009b.
[56] S. Kumar, V. K. Lamba, S. Jangra, “Image Quality Analysis of Segmented Iris using Filters,”, Int. J. of Recent Technology and Engineering, Vol. 7, No. 5, pp. 279-296, Jan. 2019.
[57] L. Dhir, N. E. Habib, D. M. Monro, and S. Rakshit, “Effect of cataract surgery and pupil dilation on iris pattern recognition for personal authentication”, Eye, 224(6):1006–1010, 2010.