Skip to main content
SpringerLink
Log in

A comprehensive review on iris image-based biometric system

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Iris image-based biometric systems are commonly used in applications that demand security, authentication, recognition and faster login access. In solving these real-time problems, the impact of soft computing techniques which employ cognitive skills is very high. Although this system has been commercialized, the scope for improvement is still plenty. This paper introduces the reader to different segments of an iris recognition system and reviews the techniques involved with each segment. It reports on how research articles validate the robustness of an iris-based recognition system. As these systems are fallible, it also shows the vulnerabilities associated with each segment and provides insights to develop much better intelligent and robust techniques which will make the system more accurate. This paper also shows that in spite of versatility of soft computing techniques, it is not fully exploited for iris recognition systems. The present challenges and directions for future research are also discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Abate AF, Frucci M, Galdi C, Riccio D (2015) BIRD: watershed based iris detection for mobile devices. Pattern Recogn Lett 57:43–51

    Google Scholar 

  • Abdullah MAM, Dlay SS, Woo WL, Chambers JA (2017) Robust iris segmentation method based on a new active contour force with a noncircular normalization. IEEE Trans Syst Man Cybern: Syst 47(12):3128–3141

    Google Scholar 

  • Abhyankar A, Schuckers S (2010) A novel biorthogonal wavelet network system for off-angle iris recognition. Pattern Recogn 43(3):987–1007

    MATH  Google Scholar 

  • Abidin ZZ, Manaf M, Shibghatullah AS, Anawar S, Ahmad R (2013) Feature extraction from epigenetic traits using edge detection in iris recognition system. In: 2013 IEEE international conference on signal and image processing applications, Melaka, pp 145–149

  • Ahamed A, Bhuiyan MIH (2012) Low complexity iris recognition using curvelet transform. In: 2012 International conference on informatics, electronics and vision (ICIEV), Dhaka, pp 548–553

  • Ahmadi N, Akbarizadeh G (2018) Hybrid robust iris recognition approach using iris image pre-processing, two-dimensional gabor features and multi-layer perceptron neural network/PSO. IET Biom 7(2):153–162

    Google Scholar 

  • Ali HS, Ismail AI, Farag FA, Abd El-Samie FE (2016) Speeded up robust features for efficient iris recognition. SIViP 10(8):1385–1391

    Google Scholar 

  • Alonso-Fernandez F, Tome-Gonzalez P, Ruiz-Albacete V, Ortega-Garcia J (2009) Iris recognition based on sift features. In: Proceedings of international conference on biometrics, identity and security, New York, pp 1–8

  • Alvarez-Betancourt Y, Garcia-Silvente M (2016) A keypoints-based feature extraction method for iris recognition under variable image quality conditions. Knowl-Based Syst 92:169–182

    Google Scholar 

  • American National Standards Institute (1988) American national standard for the safe use of lasers and LEDs used in optical fiber transmission systems. ANSI Z136:2

    Google Scholar 

  • Amir A, Zimet L, Sangiovanni-Vincentelli A, Kao S (2005) An embedded system for an eye-detection sensor. Comput Vis Image Underst 98(1):104–123

    Google Scholar 

  • Arivazhagan S, Priyadharshini SS, Sekar JR (2011) Iris recognition using Ridgelet transform. In: International conference on recent advancements in electrical, electronics and control engineering, India, pp 286–290

  • Arsalan M et al (2017) Deep learning-based iris segmentation for iris recognition in visible light environment. Symmetry (Basel) 9(11):263

    Google Scholar 

  • Aydi W, Masmoudi N, Kamoun L (2011) New corneal reflection removal method used in iris recognition system. Int J Electron Commun Eng 5(5):697–701

    Google Scholar 

  • Bae K, Noh S, Kim J (2003) Iris feature extraction using independent component analysis. In: Proceedings of 4th international conference audio- and video-based biometric person authentication, Springer, Berlin, pp 838–844

  • Bakshi S, Mehrotra H, Majhi B (2011) Real-time iris segmentation based on image morphology. In: Proceedings of the 2011 international conference on communication, computing & security (ICCCS ‘11). ACM, New York, NY, USA, pp 335–338

  • Bakshi S, Mehrotra H, Raman R, Sa PK (2012) Score level fusion of SIFT and SURF for IRIS. In: 2012 International conference on devices, circuits and systems (ICDCS), Coimbatore, India, IEEE, pp 527–531

  • Barpanda SS et al (2018a) Iris feature extraction through wavelet mel-frequency cepstrum coefficients. Opt Laser Technol. https://doi.org/10.1016/j.optlastec.2018.03.002

    Google Scholar 

  • Barpanda SS, Sa PK, Marques O, Majhi B, Bakshi S (2018b) Iris recognition with tunable filter bank based feature. Multimed Tools Appl 77(6):76371–77674

    Google Scholar 

  • Basha AJ, Palanisamy V, Purusothaman T (2011) Efficient multimodal biometric authentication using fast fingerprint verification and enhanced iris features. J Comput Sci 7(5):698–706

    Google Scholar 

  • Bay H, Ess A, Tuytelaars T, Van Gool L (2008) SURF: speeded up robust features. Comput Vis Image Underst 110(3):346–359

    Google Scholar 

  • Belcher C, Du Y (2009) Region-based sift approach to iris recognition. Opt Lasers Eng 47(1):139–147

    Google Scholar 

  • Benaliouche H, Touahria M (2014) Comparative study of multimodal biometric recognition by fusion of iris and fingerprint. Sci World J 2014(829369):1–13

    Google Scholar 

  • Bendale A, Nigam A, Prakash S, Gupta P (2012) Iris segmentation using improved hough transform. In: Proceedings of international conference on intelligent computing, Heidelberg, pp 408–415

  • Bhateja AK, Sharma S, Chaudhury S, Agrawal N (2016) Iris recognition based on sparse representation and k-nearest subspace with genetic algorithm. Pattern Recogn Lett 73(April):13–18

    Google Scholar 

  • Burge MJ, Bowyer KW (2013) Handbook of iris recognition. Springer, New York

    Google Scholar 

  • Camus TA, Wildes R (2002) Reliable and fast eye finding in close-up images. In: Object recognition supported by user interaction for service robots, Canada, pp 389–394

  • Chen C, Chu C (2009) High performance iris recognition based on 1-D circular feature extraction and PSO–PNN classifier. Expert Syst Appl 36(7):10351–10356

    Google Scholar 

  • Chen K, Chou C, Shih S (2007) Feature selection for iris recognition with AdaBoost. In: International conference on intelligent information hiding and multimedia signal processing, Taiwan, pp 411–414

  • Costa RMD, Gonzaga A (2012) Dynamic features for iris recognition. IEEE Trans Syst Man Cybern Part B (Cybernatics) 42(4):1072–1082

    Google Scholar 

  • Cui J, Wang Y, Tan T, Ma L, Sun Z (2004) A fast and robust iris localization method based on texture segmentation. Proc SPIE 5404:401–408

    Google Scholar 

  • Daugman J (1993) High confidence visual recognition of persons by a test of statistical significance. IEEE Trans Pattern Anal Mach Intell 15(11):1148–1161

    Google Scholar 

  • Daugman J (1998) Phenotypic versus genotypic approaches to face recognition: from theory to applications. Springer, New York, pp 108–123

    Google Scholar 

  • Daugman J (2004a) Iris recognition border-crossing system in the UAE. Int Airpt Rev 8(2):35

    Google Scholar 

  • Daugman J (2004b) How iris recognition works. IEEE Trans Circuits Syst Video Technol 14(1):21–30

    Google Scholar 

  • Daugman J (2007) New methods in iris recognition. IEEE Trans Syst Man Cybern Part B (Cybernatics) 37(5):1167–1175

    Google Scholar 

  • Dehkordi AB, Abu-Bakar SAR (2013) Noise reduction in iris recognition using multiple thresholding. In: International conference on signal and image processing applications, Malaysia, pp 140–144

  • Eskandari M, Toygar Ö, Demirel H (2014) Feature extractor selection for face-iris multimodal recognition. SIViP 8(6):1189–1198

    Google Scholar 

  • Farihan A, Raffei M, Asmuni H, Hassan R, Othman RM (2013) Feature extraction for different distances of visible reflection iris using multiscale sparse representation of local Radon transform. Pattern Recogn 46(10):1–12

    Google Scholar 

  • Farouk RM, Kumar R, Riad KA (2011) Iris matching using multi-dimensional artificial neural network. IET Comput Vision 5(3):178–184

    MathSciNet  Google Scholar 

  • Fatt RNY, Haur TY, Ming MK (2009) Iris verification algorithm based on texture analysis and its implementation on DSP. In: 2009 International conference on signal acquisition and processing, Kuala Lumpur, pp 198–202

  • Fernández C, Pérez D, Segura C, Hernando J (2012) A novel method for low-constrained iris boundary localization. In: 2012 5th IAPR international conference on biometrics (ICB), New Delhi, India, pp 291–296

  • Flom L, Safir A (1987) Iris recognition system, U.S. Patent 4 641 349

  • Galdi C, Dugelay JL (2017) FIRE: fast iris recognition on mobile phones by combining colour and texture features. Pattern Recognit Lett 91(May):1–8

    Google Scholar 

  • Gamal AE, Eltoukhy H (2005) CMOS image sensors. IEEE Circuits Devices Mag 21(3):6–20

    Google Scholar 

  • Gaxiola F, Melin P, Valdez F, Castro JR (2018) Person recognition with modular deep neural network using the iris biometric measure. In: Castillo O, Melin P, Kacprzyk J (eds) Fuzzy logic augmentation of neural and optimization algorithms: theoretical aspects and real applications. Studies in computational intelligence, vol 749. Springer, Cham

    Google Scholar 

  • Gong Y, Zhang D, Shi P, Yan J (2012) High-speed multispectral iris capture system design. IEEE Trans Instrum Meas 61(7):1966–1978

    Google Scholar 

  • Grabowski K, Napieralski A (2011) Hardware architecture optimized for iris recognition. IEEE Trans Circuits Syst Video Technol 21(9):1293–1303

    Google Scholar 

  • Guesmi H, Trichili H, Alimi AM, Solaiman B (2012) Iris verification system based on curvelet transform. In: 2012 IEEE 11th international conference on cognitive informatics and cognitive computing, Kyoto, pp 226–229

  • Han YL, Min TH, Park R (2015) Efficient iris localisation using a guided filter. IET Image Proc 9(5):405–412

    Google Scholar 

  • Harjoko A, Hartati S, Dwiyasa H (2009) Method for iris recognition based on 1d coiflet wavelet. World Acad Sci Eng Technol 3(8):1513–1516

    Google Scholar 

  • Hashim N, Abidin ZZ, Shibghatullah A, Abas ZA, Yusof N (2015) A new model of crypt edge detection using PSO and Bi-cubic interpolation for iris recognition. In: Sulaiman AH, Othman AM, Othman IMF, Rahim AY, Pee CN (eds) Advanced computer and communication engineering technology: proceedings of ICOCOE 2015. Springer, Cham, pp 659–669

    Google Scholar 

  • He Y, Cui J, Tan T, Wang Y (2006) Key techniques and methods for imaging iris in focus. In: International conference on pattern recognition, China, pp 557–561

  • He X, An S, Shi P (2007) Statistical texture analysis-based approach for fake iris detection using support vector machine. In: Proceedings of international conference on biometrics 2007, Springer, Berlin, pp 540–546

  • He Z, Sun Z, Tan T, Wei Z (2009) Efficient iris spoof detection via boosted local binary patterns. In: International conference on Biometrics, Springer, Berlin, pp 1087–1097

  • Hematian A, Chuprat S, Manaf AA, Yazdani S, Parsazadeh N (2013) Real-time FPGA-based human iris recognition embedded system: zero delay human iris feature extraction. Adv Intell Syst Comput 209:195–204

    Google Scholar 

  • Hilal A, Beauseroy P, Daya B (2014) Elastic strips normalisation model for higher iris recognition performance. IET Biom 3(4):190–197

    Google Scholar 

  • Hollingsworth K, Peters T, Bowyer KW, Flynn PJ (2009) Iris recognition using signal-level fusion of frames from video. IEEE Trans Inf Forensics Secur 4(4):837–848

    Google Scholar 

  • Howard JJ, Etter DM (2014) A statistical investigation into the stability of iris recognition in diverse population sets. Biom Surveill Technol Hum Act Identif 9075:907508

    Google Scholar 

  • Hu Y, Sirlantzis K, Howells G (2015) Iris liveness detection using regional features. Pattern Recogn Lett 82(2):242–250

    Google Scholar 

  • Huang X, Ren L, Yang R (2009) Image deblurring for less intrusive iris capture. In: 2009 IEEE computer society conference on computer vision and pattern recognition workshops, CVPR workshops 2009, USA, pp 1558–1565

  • Huang J, You X, Yuan Y, Yang F, Lin L (2010) Rotation invariant iris feature extraction using gaussian markov random fields with non-separable wavelet. Neurocomputing 73(4–6):883–894

    Google Scholar 

  • IRIS ID: Iris access in action. http://www.irisid.com/productssolutions/irisaccessinaction/. Accessed 30 Apr 2018

  • Iris Scans at Amsterdam Airport Schiphol. http://www.schiphol.nl/Travellers/AtSchiphol/Privium.htm. Accessed 30 Apr 2018

  • 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

  • Kang JS (2010) Mobile iris recognition systems: an emerging biometric technology. Proc Comput Sci 1(1):475–484

    Google Scholar 

  • Kang BJ, Park KR (2007) Real-time image restoration for iris recognition systems. IEEE Trans Syst Man Cybern Part B (Cybernatics) 37(6):1555–1566

    Google Scholar 

  • Kang BJ, Park KR (2009) A new multi-unit iris authentication based on quality assessment and score level fusion for mobile phones. Mach Vis Appl 21(4):541–553

    Google Scholar 

  • Karakaya M (2016) A study of how gaze angle affects the performance of iris recognition. Pattern Recogn Lett 82(2):132–143

    MathSciNet  Google Scholar 

  • Kaur B, Singh S, Kumar J (2018) Robust iris recognition using moment invariants. Wireless Pers Commun 99(2):799–828

    Google Scholar 

  • Kennell LR, Ives RW, Gaunt RM (2006) Binary morphology and local statistics applied to iris segmentation for recognition. In: International conference on image processing, ICIP, USA, pp 293–296

  • Kim D, Jung Y, Toh KA, Son B, Kim J (2016) An empirical study on iris recognition in a mobile phone. Expert Syst Appl 54(July):328–339

    Google Scholar 

  • Ko J, Gil Y, Yoo J, Chung K (2007) A novel and efficient feature extraction method for iris recognition. ETRI J 29(3):399–401

    Google Scholar 

  • Koh J, Govindaraju V, Chaudhary V (2010) A robust iris localization method using an active contour model and hough transform. In: 2010 20th international conference on pattern recognition, Istanbul, pp 2852–2856

  • Kong W, Zhang D (2003) Detecting eyelash and reflection for accurate iris segmentation. Int J Pattern Recognit Artif Intell 17(852):1025–1034

    Google Scholar 

  • Krichen E, Allano L, Garcia-Salicetti S, Dorizzi B (2005) Specific texture analysis for iris recognition. In: International conference on audio- and video-based biometric person authentication, Springer, Berlin, pp 23–30

  • Kumar DRS et al (2011) Iris recognition based on DWT and PCA. In: 2011 International conference on computational intelligence and communication networks, Gwalior, pp 489–493

  • Kumar DRS, Raja KB, Chhootaray RK, Pattnaik S (2011) PCA based iris recognition using DWT. Int J Comput Technol Appl 2(4):884–893

    Google Scholar 

  • Kumar S, Singh SK, Abidi AI, Datta D, Sangaiah AK (2017) Group sparse representation approach for recognition of cattle on muzzle point images. Int J Parallel Program. https://doi.org/10.1007/s10766-017-0550-x

    Google Scholar 

  • Kumar V, Asati A, Gupta A (2018) Hardware accelerators for iris localization. J Signal Process Syst 90(4):655–671

    Google Scholar 

  • Li JC (2009) Fast computation for iris normalization. Thesis, Graduate Institute Community Engineering, National Chi Nan University, Puli, Taiwan

  • Li Y, Huang P (2017) An accurate and efficient user authentication mechanism on smart glasses based on iris recognition. Mob Inf Syst 2017(1281020):1–14

    Google Scholar 

  • Li H, Sun Z, Tan T (2012) Robust iris segmentation based on learned boundary detectors. In: 5th IAPR international conference on biometrics (ICB), New Delhi, pp 317–322

  • Liao X, Yin J, Guo S, Li X, Sangaiah AK (2018) Medical JPEG image steganography based on preserving inter-block dependencies. Comput Electr Eng 67:320–329

    Google Scholar 

  • Lili P, Mei X (2005) The algorithm of iris image preprocessing. In: Fourth IEEE workshop on automatic identification advanced technologies (AutoID’05), USA, pp 134–138

  • Liu J, Sun Z, Tan T (2013) Recognition of motion blurred iris images. In: 2013 IEEE sixth international conference on biometrics: theory, applications and systems (BTAS), Arlington, VA, pp 1–7

  • Liu J, Sun Z, Tan T (2014) Distance metric learning for recognizing low-resolution iris images. Neurocomputing 144:484–492

    Google Scholar 

  • Liu N, Zhang M, Li H, Sun Z, Tan T (2015) Deepiris: learning pairwise filter bank for heterogeneous iris verification. Pattern Recogn Lett 82(2):154–161

    Google Scholar 

  • Liu N, Li H, Zhang M, Liu J, Sun Z, Tan T (2016) Accurate iris segmentation in non-cooperative environments using fully convolutional networks. In: 2016 international conference on biometrics (ICB), Halmstad, pp 1–8

  • Liu-Jimenez J, Sanchez-Reillo R, Fernandez-Saavedra B (2011) Iris biometrics for embedded systems. IEEE Trans Very Large Scale Integr Syst 19(2):274–282

    Google Scholar 

  • Lulé T et al (2000) Sensitivity of CMOS based imagers and scaling perspectives. IEEE Trans Electron Devices 47(11):2110–2122

    Google Scholar 

  • Ma L, Tan T, Wang Y, Zhang D (2004a) Efficient iris recognition by characterizing key local variations. IEEE Trans Image Process 13(6):739–750

    Google Scholar 

  • Ma L, Tan T, Wang Y, Zhang D (2004b) Efficient iris recognition by characterizing key local variations. IEEE Trans Image Process 13(6):739–750

    Google Scholar 

  • Mehrotra H, Sa PK, Majhi B (2013) Fast segmentation and adaptive SURF descriptor for iris recognition. Math Comput Model 58(1–2):132–146

    Google Scholar 

  • Minaee S, Abdolrashidi A, Wang Y (2016a) An experimental study of deep convolutional features for iris recognition. In: IEEE signal processing in medicine and biology symposium, Philadelphia, pp 1–6

  • Minaee S, Abdolrashidiy, A, Wang Y (2016b) An experimental study of deep convolutional features for iris recognition. In: 2016 IEEE signal processing in medicine and biology symposium (SPMB), Philadelphia, PA, pp 1–6

  • Misztal KT, Spurek P, Saeed E, Saeed K (2015) Cross entropy clustering approach to iris segmentation for biometrics purpose. Schedae Informaticae 24:29–38

    Google Scholar 

  • Monro DM, Rakshit S, Member S (2007) DCT-based iris recognition. IEEE Trans Pattern Anal Mach Intell 29(4):586–595

    Google Scholar 

  • Nabti M, Bouridane A (2008) An effective and fast iris recognition system based on a combined multiscale feature extraction technique. Pattern Recogn 41(3):868–879

    MATH  Google Scholar 

  • Nandakumar K, Jain AK, Nagar A (2008) Biometric template security. EURASIP J Adv Signal Process 2008(13):113

    Google Scholar 

  • Neagoe T, Karjala E, Banica L (2010) Why ARM processors are the best choice for embedded low-power applications? In: IEEE 16th international symposium for design and technology electronic packaging (SIITME), Romania, pp 253–258

  • Ngo H, Shafer J, Ives R, Rakvic R, Broussard R (2012) Real time iris segmentation on FPGA. In: 2012 IEEE 23rd international conference on application-specific systems, architectures and processors, Delft, pp 1–7

  • Nguyen K, Fookes C, Ross A, Sridharan S (2017a) Iris recognition with off-the-shelf CNN features: a deep learning perspective. IEEE Access 6:18848–18855

    Google Scholar 

  • Nguyen K, Fookes C, Ross A, Sridharan S (2017b) Iris recognition with off-the-shelf CNN features: a deep learning perspective. IEEE Access 6:18848–18855

    Google Scholar 

  • Ouabida E, Essadique A, Bouzid A (2017) Vander Lugt Correlator based active contours for iris segmentation and tracking. Expert Syst Appl 71(1):383–395

    Google Scholar 

  • Park HA, Park KR (2007) Iris recognition based on score level fusion by using SVM. Pattern Recogn Lett 28(15):2019–2028

    Google Scholar 

  • Proenc H (2010) Iris recognition: on the segmentation of degraded images acquired in the visible wavelength. IEEE Trans Pattern Anal Mach Intell 32(8):1502–1516

    Google Scholar 

  • Proenca H, Alexandre LA (2006) Iris segmentation methodology for non-cooperative recognition. IEE Proc Vis Image Signal Process 153(2):199–205

    Google Scholar 

  • Proenca H, Alexandre L (2007) Iris recognition: an entropy-based coding strategy robust to noisy imaging environments. In: Advances in visual computing. Lecture notes in computer science, vol 4841, Springer

  • Puhan NB, Sudha N, Kaushalram AS (2011) Efficient segmentation technique for noisy frontal view iris images using Fourier spectral density. Signal Image Video Process 5(1):105–119

    Google Scholar 

  • Pundlik S, Woodard D, Birch S (2010) Iris segmentation in non-ideal images using graph cuts. Image Vis Comput 28(12):1671–1681

    Google Scholar 

  • Radha N, Kavitha A (2012) Rank level fusion using fingerprint and iris biometrics. Indian J Comput Sci Eng 2(6):917–923

    Google Scholar 

  • Radman A, Jumari K, Zainal N (2013) Fast and reliable iris segmentation algorithm. IET Image Proc 7(1):42–49

    Google Scholar 

  • Radman A, Zainal N, Azmin S (2017) Automated segmentation of iris images acquired in an unconstrained environment using HOG-SVM and GrowCut. Digit Signal Proc 64:60–70

    MathSciNet  Google Scholar 

  • Rahulkar AD, Holambe RS (2012) Half-iris feature extraction and recognition using a new class of biorthogonal triplet half-band filter bank and flexible k-out-of-n: a postclassifier. IEEE Trans Inf Forensics Secur 7(1):230–240

    Google Scholar 

  • Rahulkar AD, Jadhav DV, Holambe RS (2012) Fast discrete curvelet transform based anisotropic iris coding and recognition using k-out-of-n: a fused post-classifier. Mach Vis Appl 23(6):1115–1127

    Google Scholar 

  • Rai H, Yadav A (2014) Expert Systems with Applications Iris recognition using combined support vector machine and Hamming distance approach. Expert Syst Appl 41(2):588–593

    Google Scholar 

  • Raja KB, Raghavendra R, Krishna V, Busch C (2015) Smartphone based visible iris recognition using deep sparse filtering. Pattern Recognit Lett 57:33–42

    Google Scholar 

  • Rakvic RN, Ulis BJ, Broussard RP, Ives RW, Steiner N (2009) Parallelizing iris recognition. IEEE Trans Inf Forensics Secur 4(4):812–823

    Google Scholar 

  • Rakvic R, Broussard R, Ngo HAU (2016) Energy efficient iris recognition with graphics processing units. IEEE Access 4:2831–2839

    Google Scholar 

  • Rathgeb C, Uhl A, Wild P (2012) Iris biometrics: from segmentation to template security. Springer, New York

    Google Scholar 

  • Rizzolo S, Goiffon V, Estribeau M, Marcelot O, Martin-Gonthier P, Magnan P (2018) Influence of pixel design on charge transfer performances in CMOS image sensors. IEEE Trans Electron Devices 65(3):1048–1055

    Google Scholar 

  • Ross A, Shah S (2006) Segmenting non-ideal irises using geodesic active contours. In: Biometrics symposium, USA, pp 8–13

  • Roy K, Bhattacharya P (2008a) Improving features subset selection using genetic algorithms for iris recognition. In: Prevost L, Marinai S, Schwenker F (eds) Artificial neural networks in pattern recognition. Lecture notes in computer science. Springer, Berlin, pp 292–304

    Google Scholar 

  • Roy K, Bhattacharya P (2008b) Optimal features subset selection and classification for iris recognition. EURASIP J Image Video Process 2008(9):1–20

    Google Scholar 

  • Roy K, Bhattacharya P, Suen CY (2011) Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs. Eng Appl Artif Intell 24(3):458–475

    Google Scholar 

  • Ryan WJ, Woodard DL, Duchowski AT, Birchfield ST (2008) Adapting starburst for elliptical iris segmentation. In: 2008 IEEE second international conference on biometrics: theory, applications and systems, Arlington, VA, pp 1–7

  • Saad IA, George LE, Tayyar AA (2014) Accurate and fast pupil localization stretching, seed filling and circular geometrical constraints. J Comput Sci 10(2):305–315

    Google Scholar 

  • Sahmoud SA, Abuhaiba IS (2013) Efficient iris segmentation method in unconstrained environments. Pattern Recogn 46(12):3174–3185

    Google Scholar 

  • Sahu B, Kumar P, Bakshi S, Sangaiah AK (2018) Reducing dense local feature key-points for faster iris recognition. Computers and Electrical Engineering. Elsevier, New York

    Google Scholar 

  • Saleh IA, Alzoubiady LM (2014) Decision level fusion of iris and signature biometrics for personal identification using ant colony optimization. Int J Eng Innov Technol (IJEIT) 3:35–42

    Google Scholar 

  • Saleh B, Teich M (1991) Fundamentals of photonics. Wiley, New York

    Google Scholar 

  • Sanchez-Avila C, Sanchez-Reillo R (2005) Two different approaches for iris recognition using gabor filters and multiscale zero-crossing representation. Pattern Recogn 38(2):231–240

    Google Scholar 

  • Sanchez-Avila C, Sanchez-Reillo R, Martin-Roche DD (2002) Iris-based biometric recognition using dyadic wavelet transform. IEEE Aerosp Electron Syst Mag 17(10):3–6

    Google Scholar 

  • Sangaiah AK, Samuel OW, Li X, Abdel-Basset M, Wang H (2017) Towards an efficient risk management in software projects-fuzzy reinforcement paradigm. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2017.07.022

    Google Scholar 

  • Sardar M, Mitra S, Shankar BU (2018) Iris localization using rough entropy and CSA: a soft computing approach. Appl Soft Comput 67:61–69

    Google Scholar 

  • Schuckers SAC, Schmid NA, Abhyankar A, Dorairaj V, Boyce CK, Hornak LA (2007) On techniques for angle compensation in nonideal iris recognition. IEEE Trans Syst Man Cybern Part B Cybern 37(5):1176–1190

    Google Scholar 

  • Shah S, Ross A (2009) Iris segmentation using geodesic active contours. IEEE Trans Inf Forensics Secur 4(4):824–836

    Google Scholar 

  • Shams MY, Rashad MZ, Nomir O, El-Awady RM (2011) Iris recognition based on LBP and combined LVQ classifier. IJCSIT 3(5):67

    Google Scholar 

  • Shamsi M, Rasouli A (2011) An innovative trapezium normalization for iris recognition systems. In: International conference on computer and software modelling IPCSIT, Singapore, vol 14, pp 118–122

  • Shin KY, Nam GP, Jeong DS, Cho DH, Kang BJ, Park KR, Kim J (2012) New iris recognition method for noisy iris images. Pattern Recogn Lett 33(8):991–999

    Google Scholar 

  • Si Y, Mei J, Karimi HR, Wang C, Gao H (2012) Design and implementation of a low-cost embedded iris recognition system on a dual-core processor platform. IFAC Proc Vol 45(4):278–282

    Google Scholar 

  • Sik D et al (2010) A new iris segmentation method for non-ideal iris images. Image Vis Comput 28(2):254–260

    Google Scholar 

  • Subban R, Susitha N, Mankame DP (2017) Efficient iris recognition using Haralick features based extraction and fuzzy particle swarm optimization. Cluster Comput. https://doi.org/10.1007/s10586-017-0934-0

    Google Scholar 

  • Sun Z, Wang Y, Tan T, Cui J (2005) Improving iris recognition accuracy via cascaded classifiers. IEEE Trans Syst Man Cybern Part C (Applications and Reviews) 35(3):435–441

    Google Scholar 

  • Sun Z, Zhang H, Tan T, Wang J (2014) Iris image classification based on hierarchical visual codebook. IEEE Trans Pattern Anal Mach Intell 36(6):1120–1133

    Google Scholar 

  • Sundaram RM, Dhara BC, Chanda B (2011) A fast method for iris localization. In: 2011 Second international conference on emerging applications of information technology, Kolkata, India, pp 89–92

  • Talal M, Khan TM, Khan SA, Khan MA, Guan L (2012) Iris localization using local histogram and other image statistics. Opt Lasers Eng 50(5):645–654

    Google Scholar 

  • Tallapragada VVS, Rajan EG (2012) Improved kernel-based IRIS recognition system in the framework of support vector machine and hidden markov model. IET Image Proc 6(6):661–667

    Google Scholar 

  • Tan C, Kumar A (2012) Unified framework for automated iris acquired face images. IEEE Trans Image Process 21(9):4068–4079

    MathSciNet  MATH  Google Scholar 

  • Tan T, Wang Y, Ma L (2012) A new sensor for live iris imaging. PR China Patent ZL 01278644:6

    Google Scholar 

  • Tapia J, Aravena C (2017) Gender classification from NIR iris images using deep learning. In: Bhanu B, Kumar A (eds) Deep learning for biometrics. Advances in computer vision and pattern recognition. Springer, Cham, pp 219–239

    Google Scholar 

  • The Child Project. The child project—home. The child project, 25 09 2007. http://www.thechildproject.org/. Accessed 30 Apr 2018

  • Tomeo-Reyes I, Ross A, Clark AD, Chandran V (2015) A biomechanical approach to iris normalization. In: 2015 International conference on biometrics (ICB), Phuket, pp 9–16

  • Tsai CC, Lin HY, Taur J, Tao CW (2012) Iris recognition using possibilistic fuzzy matching on local features. IEEE Trans Syst Man Cybern Part B (Cybernatics) 42(1):150–162

    Google Scholar 

  • UK Border Agency Iris recognition immigration system. http://www.bbc.com/news/uk-england-17058448. Accessed 30 Apr 2018

  • UNHCR - Iris testing of returning Afghans passes 200,000 mark. http://www.unhcr.org/3f86b4784.html. Accessed 30 Apr 2018

  • Unique Identification Authority of India (2012) Planning Commission, Government of India, Aadhaar services—resident portal Government of India. https://uidai.gov.in/enrolment-update/aadhaar-enrolment.html. Accessed 30 Apr 2018

  • University of Tehran, University of Tehran iris image respository. https://utiris.wordpress.com/. Accessed 30 Apr 2018

  • Vatsa M, Singh R, Noore A (2008) Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Trans Syst Man Cybern Part B (Cybernatics) 38(4):1021–1035

    Google Scholar 

  • Viriri S, Tapamo J (2017) Iris pattern recognition based on cumulative sums and majority vote methods. Int J Adv Rob Syst 14(3):1–9

    Google Scholar 

  • Wang Y, Han JQ (2005) Iris recognition using independent component analysis. In: Proceedings of 4th international conference on machine learning and cybernetics, Guangzhou, China, vol 7, pp 4487–4492

  • Wang YB, He YQ, Hou YS, Liu T (2008) Design method of ARM based embedded iris recognition system. In: Related technologies and applications. International symposium on photoelectron. Detection and imaging 2007; 66251G

  • Wang H, Lin S, Ye X, Gu W (2008b) Separating corneal reflections for illumination estimation. Neurocomputing 71(10–12):1788–1797

    Google Scholar 

  • Wang Z, Han Q, Niu X, Busch C (2009) Feature-level fusion of iris and face for personal identification. In: Proceedings of the 6th international symposium on neural networks (ISNN 2009): advances in neural networks—part III, pp 356–364

  • Wang Q, Zhang X, Li M, Dong X, Zhou Q, Yin Y (2012) Adaboost and multi-orientation 2D gabor-based noisy iris recognition. Pattern Recogn Lett 33(8):978–983

    Google Scholar 

  • Wei Z, Tan T, Sun Z (2007) Nonlinear iris deformation correction based on Gaussian model. In: International conference on biometrics, Springer, Berlin, pp 780–789

  • Wild P, Hofbauer H, Ferryman J, Uhl A (2015) Segmentation-level fusion for iris recognition. In: 2015 International conference of the biometrics special interest group (BIOSIG), Darmstadt, pp 1–6

  • Wildes RP et al (1994) A system for automated iris recognition. In: Proceedings of 1994 IEEE workshop on applications of computer vision, Sarasota, FL, pp 121–128

  • Wildes RP (1997) Iris recognition: an emerging biometric technology. Proc IEEE 85(9):1348–1363

    Google Scholar 

  • Wildes RP, Asmuth JC, Green GL, Hsu SC, Kolczynski RJ, Matey JR, McBride SE (1996) A machine vision system for iris recognition. Mach Vis Applicat. 9(1):1–8

    Google Scholar 

  • Yao P, Li J, Ye X, Zhuang Z, Li B (2006) Iris recognition algorithm using modified log-gabor filters. In: 18th International conference on pattern recognition (ICPR’06), Hong Kong, pp 461–464

  • Yuan X, Shi P (2005) A non-linear normalization model for iris recognition. Proceeding of Advances in Biometric Person Authentication. Springer, Berlin, pp 135–141

    Google Scholar 

  • Zaim A (2005) Automatic segmentation of iris images for the purpose of identification. In: IEEE international conference on image processing 2005, ICIP, Italy, vol 3, pp 273–276

  • Zhang W, Wang C (2017) Application of convolution neural network in iris recognition technology. In: The 2017 4th international conference on systems and informatics (ICSAI 2017), China, pp 1169–1174

  • Zhang P, Li D, Wang Q (2004) A novel iris recognition method based on feature fusion. In: Proceedings of 2004 international conference on machine learning and cybernetics (IEEE Cat. No. 04EX826), pp 3661–3665

  • Zhang D, Monro D, Rakshit S (2006) Eyelash removal method for human iris recognition. In: 2006 International conference on image processing, USA, pp 285–288

  • Zhang M, Sun Z, Tan T (2012) Perturbation-enhanced feature correlation filter for robust iris recognition. IET Biom 1(1):37–45

    Google Scholar 

  • Zhao Z, Kumar A (2015) An accurate iris segmentation framework under relaxed imaging constraints using total variation model. In: 2015 IEEE international conference on computer vision (ICCV), Santiago, pp 3828–3836

  • Zheng Z, Yang J, Yang L (2005) A robust method for eye features extraction on color image. Pattern Recogn Lett 26(14):2252–2261

    Google Scholar 

  • Zhou Y, Kumar A (2010) Personal identification from iris images using localized radon transform. In: 2010 20th international conference on pattern recognition, Istanbul, pp 2840–2843

  • Zhu R, Yang J, Wu R (2006) Iris recognition based on local feature point matching. In: 2006 International symposium on communications and information technologies, Bangkok, pp 451–454

  • Zuo J, Schmid NA (2010) On a methodology for robust segmentation of nonideal iris images. IEEE Trans Syst Man Cybern Part B (Cybernatics) 40(3):703–718

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Karunya University, Coimbatore, India

    J. Jenkin Winston & D. Jude Hemanth

Authors
  1. J. Jenkin Winston
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Jude Hemanth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Jude Hemanth.

Ethics declarations

Conflict of interest

The author declares that they have no conflict of interest.

Additional information

Communicated by A.K. Sangaiah, H. Pham, M.-Y. Chen, H. Lu, F. Mercaldo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Winston, J.J., Hemanth, D.J. A comprehensive review on iris image-based biometric system. Soft Comput 23, 9361–9384 (2019). https://doi.org/10.1007/s00500-018-3497-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3497-y

Keywords

Search

Navigation