Revisiting Iris Recognition with Color Cosmetic Contact Lenses

Naman Kohli∗, Daksha Yadav∗, Mayank Vatsa, Richa SinghIIIT - Delhi

Email: {naman09027, daksha09019, mayank, rsingh}@iiitd.ac.in

Abstract

Over the years, iris recognition has gained importancein the biometrics applications and is being used in severallarge scale nationwide projects. Though iris patterns areunique, they may be affected by external factors such as il-lumination, camera-eye angle, and sensor interoperability.The presence of contact lens, particularly color cosmeticlens, may also pose a challenge to iris biometrics as it ob-fuscates the iris patterns and changes the inter and intra-class distributions. This paper presents an in-depth analysisof the effect of contact lens on iris recognition performance.We also present the IIIT-D Contact Lens Iris database withover 6500 images pertaining to 101 subjects. For eachsubject, images are captured without lens,transparent (pre-scription) lens, and color cosmetic lens (textured) using twodifferent iris sensors. The results computed using VeriEyesuggest that color cosmetic lens significantly increases thefalse rejection at a fixed false acceptance rate. Also, the ex-periments on four existing lens detection algorithms suggestthat incorporating lens detection helps in maintaining theiris recognition performance. However, further research isrequired to build sophisticated lens detection algorithm thatcan improve iris recognition.

1. Introduction

Iris is one of the most promising biometric modalitieswhich is being used in several large scale applications suchas UAE port of entry and India’s UIDAI (Aadhar). Thoughiris features are considered to be unique, recent researchresults suggest that they are affected by several covariatessuch as pupil dilation [8] and sensor interoperability [1, 4].Another factor that may affect iris recognition and has re-ceived less attention, is the presence oftransparent and tex-tured color cosmetic lenses. With recent developments intechnology and low cost, the use of contact lens is becomingmore prevalent. According to Nichols [10], the worldwidecontact lens market in 2011 is estimated to be about 6.8 bil-

∗Equal contributions by student authors, N. Kohli and D. Yadav.

Without Lens

With Color Cosmetic Lens

Figure 1. Illustrating the variations due to color cosmetic lenses.The first row in both the figures contain images in visible spec-trum and the second row contains examples under near infraredspectrum.

lions. Contact lens are generally used to correct eyesight asa replacement for spectacles/glasses. They are however, in-creasingly being used for cosmetic reasons also where tex-ture and color of iris region is superimposed with a thin tex-tured lens. As shown in Figure 1 (iris images with and with-out color cosmetic lens), it is apparent that the use of colorcosmetic lens changes the appearance/texture of an eye inboth visible and near infrared spectrums. Therefore, we be-

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lieve that it is important to understand (1) the effect of con-tact lens on the performance of iris recognition algorithmsand (2) how this effect can be mitigated.

The discussion about fake iris images and images withcontact lens effect was first initiated by Daugman [5] wherefrequency spectrum analysis was proposed to countermea-sure against subterfuge. Lee et al. [9] later proposed amethod to distinguish between genuine and fake iris basedon the Purkinje image. On the other hand, He et al. [7]used four features (mean and standard deviation of pixelvalues, contrast and the angular momentum) of the graylevel co-occurrence matrix (GLCM) as a feature vector andSupport Vector Machine (SVM) classifier for predicting ifthe iris image contained colored contact lens or not. Ringand Bowyer [11] analyzed the iris bit code to detect regionsof local distortions which can be due to contact lenses orocclusions. Wei et al. [14] used three different featuresto detect lens in iris images. The first method is the edgesharpness detection where the sum of intensity values of in-ner boundary of the iris is subtracted from that of the outerboundary. The second feature is Iris-Textons computed us-ing Gabor filters along with KMeans algorithm and coun-terfeit iris classification is performed using SVM. The thirdapproach utilizes GLCM features and SVM classificationfor the same task. Among the three approaches, the au-thors report that Iris-Textons and GLCM features are ableto perform better classification. Baker et al. [2], on adatabase of 51 subjects wearing non-cosmetic contact lensand 64 subjects without lens, suggest that the lens wearershave 14 times higher false rejection rate compared to non-contact lens wearers. However, the number of users in thedatabase per lens type is small and the variability amonglenses is also not sufficient to statistically establish anyre-sults. Zhang et al. [15] used weighted Local Binary Pattern(LBP) encoding with SIFT descriptor and SVM for clas-sification of lens and non-lens iris images. On a databaseof 72 users, a correct classification accuracy of over 99%is achieved. Using 12,003 images from 87 non-cosmeticcontact-lens-wearing subjects and 9,697 images pertainingto 124 non-contact lens wearing subjects, Baker et al. [3]concluded that the lenses that produce larger artifacts on theiris yield more degraded performance.

The current literature primarily focuses on transparentcontact lenses and limited studies with colored cosmeticlenses. Further, many of the results reported in literatureare not evaluated using any state-of-the-art commercial sys-tems. To the best of our knowledge, the databases used inthese studies are not publicly available except the one usedby Baker et al. [3] that contains non-cosmetic lenses. Fi-nally, the lens detection approaches are not evaluated withrespect to improvement in iris recognition performance.Doyle et al. [6] conducted a three class lens detection prob-lem, in which an ensemble of 14 classifiers was learnt to

Hazel Blue

Green Gray

Figure 2. Iris images with four different types of colored lensesfrom CIBA vision.

achieve 97% accuracy. However, the accuracy of classify-ing an iris image into soft lens or no lens category was low.Also, the number of users per type of lens were less. In thisresearch, we attempt to bridge these gaps and present:

1. a new benchmark database that contains iris imageswith different kinds of contact lenses. This databaseis unique in terms of the types of images per subject,number of subjects, acquisition devices, contact lenscolors, and manufacturers,

2. baseline verification accuracies using a commercialiris recognition system to understand the effect oftransparent and colored cosmetic lenses. We alsopresent inter and intra class performance analysis ofcontact lenses and effect of different iris sensors,

3. performance comparison of existing lens detection al-gorithms across different lens types and iris sensors.

2. Effect of Contact Lenses on Iris Recognition

With the increasing use of contact lenses, multiple typesand colors of lenses are available with different textures byseveral manufacturers. To the best of our knowledge, thereis no database that captures the variations across colors andtextures in lenses. Further, different lens manufacturersmayhave different technologies for contact lens creation. To an-alyze the effect of these parameters on iris recognition, wehave prepared theIIIT-D Contact Lens Iris(CLI) database.This section presents the details of the database and the per-formance evaluation of a commercial iris recognition sys-tem in both presence and absence of contact lenses.

2.1. IIIT-D Contact Lens Iris Database

The IIIT-D CLI database is prepared with three objec-tives: (1) capture images pertaining to at least 100 sub-jects, (2) for each individual, capture images without lens,

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(a) (b)

NoLens

TransparentLens

ColoredLens

ColoredLens

NoLens

TransparentLens

ColoredLens

ColoredLens

Figure 3. Iris images in IIIT-D Contact Lens Iris Database (a) images captured using Cogent iris sensor and (b) images captured using Vistairis sensor. The two color lenses used here are from CIBA Vision in third column and Bausch & Lomb in fourth column.

Number of subjects(classes)

101 (202)

Types of contact lens Without lens, transparent,and colored

Lens manufactures CIBA Vision and Bausch& Lomb

Lens colors Blue, Gray, Hazel andGreen

Number of subjects percolored lens type

Blue (20), Gray (29),Green (30) and Hazel (22)

Iris sensors used for ac-quisition

Cogent dual iris sensor andVistaFA2E iris sensor

Number of images persubject per lens type

Minimum 5 images pereye class, per lens type

Total number of image inthe database

6570

Table 1. Details of the IIIT-D Contact Lens Iris Database.

with transparent (prescription) lens, and with color cosmeticlens, and (3) capture images with variations in iris sensorsand lenses (colors and manufacturers). Table 1 summarizesthe characteristics of the IIIT-D CLI database which com-prises of 6570 iris images pertaining to 101 subjects. Bothleft and right iris images of each subject are captured andtherefore, there are 202 iris classes. The lenses used in thedatabase are soft lenses manufactured by either CIBA Vi-sion or Bausch and Lomb. For color cosmetic lenses, four

colors are used and Figure 2 shows some iris images withdifferent color lenses from CIBA Vision. To study the ef-fect of acquisition device on contact lenses, iris images arecaptured using two iris sensors: (1) Cogent dual iris sen-sor (CIS 202) and (2) VistaFA2E single iris sensor. Thedatabase contains minimum five images of each iris class ineach of the above mentioned lens categories for both the irissensors.

2.2. Performance Evaluation of Iris Recognition

VeriEye [13], a commercial software, is used to un-derstand the effect of contact lenses on iris verification.Two sets of experiments are performed on the IIIT-D CLIdatabase to evaluate the iris verification performance:

1. Effect of color and transparent lenses: By varyingthe gallery probe combinations, the effect of differenttypes of lenses on iris recognition is analyzed.

2. Effect of acquisition device: This experiment is per-formed to analyze whether iris acquisition using dif-ferent sensors has any effect on the performance withcontact lens variations. Three experiments are per-formed:

(a) both the gallery and probe images are capturedusing the Cogent sensor

(b) both the gallery and probe images are capturedusing the Vista sensor

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(c) cross sensor gallery - probe verification experi-ment.

The verification accuracies of VeriEye are computed for theabove mentioned protocols and the results are shown in Fig-ure 4 and Tables 2 and 3. The key results are summarizedas follows:

Lens type/ Sensor Cogent Vista Cross(Gallery/Probe) Sensor

Normal - Normal 98.9 99.8 97.9Normal - Transparent 96.1 59.9 95.0Normal - Color 22.1 36.4 23.4Transparent - Transparent 96.4 99.8 47.3Transparent - Normal 96.1 57.4 48.8Transparent - Color 22.9 33.8 22.5Color - Color 50.4 63.3 5.0Color - Normal 23.0 38.2 20.4Color - Transparent 22.8 32.8 17.3

Table 2. Verification results with variations in acquisition deviceand lens type. The verification accuracy is reported at 0.01% FAR.

• The Receiver Operating Characteristic (ROC) curvesin Figure 4 show that at 0.01% false accept rate (FAR),the best performance is achieved when both the galleryand probe images are without lens (normal irises).Matching images without lens and with transparentlens also show similar trends except when both theprobe and gallery belong to Vista sensor. The factcould be attributed to the acquisition technology ofVista sensor, but since its a proprietary technology wecannot claim to it. However, the performance reducessignificantly when either one of the gallery or probeimage is with colored lens and the other is without lensor with transparent lens. It is also interesting to notethat when both gallery and probe images are with col-ored lenses, the verification accuracy reduces to only50-60%.

• It can also be observed from Figure 4 that differentacquisition devices affect the iris verification perfor-mance with variations in contact lenses. ROC curvesin Figure 4 re-establish the challenge of sensor inter-operablity - without lens gallery probe images withcross sensor matching yields1̃% lower verification ac-curacy than with same sensor matching. Similarly, asshown in Table 2, for other combinations of galleryprobe pairs, the verification accuracy at 0.01% FAR isaffected by cross sensor matching. With color galleryprobe combination, the verification accuracy drops to5%.

• VeriEye gives a score ofzerofor impostor matches andany score greater than zero denotes a genuine match.

(a)

(b)

(c)

0 0.005 0.01 0.015 0.02 0.025 0.03 0.0350

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R (

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GalNormal-ProbeTrans

GalNormal-ProbeColor

GalTrans-ProbeTrans

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GalColor-ProbeColor

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GalNormal-ProbeTrans

GalNormal-ProbeColor

GalTrans-ProbeTrans

GalTrans-ProbeNormal

GalTrans-ProbeColor

GalColor-ProbeColor

GalColor-ProbeNormal

GalColor-ProbeTrans

Figure 4. ROC curves for various experiments using VeriEye: (a)both the gallery and probe images are captured using the Cogentsensor, (b) both the gallery and probe images are captured usingthe Vista sensor, and (c) cross sensor matching.

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LensGenuine Impostor

[Min, Max] Mean [Min, Max] MeanNormal [0, 1550] 653.19 [0, 87] 0.02Transparent [0, 1345] 472.99 [0, 447] 0.03Blue Color [0, 180] 31.76 [0, 77] 0.03Hazel Color [0, 129] 20.66 [0, 67] 0.01Green Color [0, 166] 22.86 [0, 79] 0.01Gray Color [0, 160] 17.82 [0, 96] 0.03

Table 3. Minimum, maximum, mean genuine, and mean impostorscores obtained from VeriEye [13] for different lens types.

The higher the match score, the greater is the confi-dence that the match pair belongs to the same user.To understand the variation in match scores due to thepresence of contact lens, we further analyze the distri-bution of genuine and impostor scores obtained fromVeriEye. For this experiment, the gallery constitutesof images without lens while the probe is varied fromwithout lens to color lens iris images. Table 3 presentsthe mean of genuine and impostor scores along withminimum and maximum scores obtained for differentcolors lenses individually. It is observed that whencolor cosmetic lenses are used, the inter-class simi-larity increases while the intra-class similarity reducessignificantly for many of the genuine pairs. In thisdatabase, blue colored lenses show maximum varia-tion from normal iris. The results shown here are com-puted for the Cogent sensor and similar performance isobserved for the Vista sensor as well.

• To ensure that the reduction in performance is due tothe presence of lens only and not because of someother factors such as poor image quality or blurriness,we analyze the images that are wrongly accepted orrejected by the matchers. Figure 5 shows that whentwo good quality normal iris images (without any lens)are matched, VeriEye produces a high similarity score.However, when the iris of the same subject while wear-ing colored contact lens is matched with the normalimage, it produces zero similarity score. This clearlyindicates that the presence of contact lens alters the ap-pearance of iris texture and may lead to incorrect veri-fication results. Similarly, for impostor pairs, as shownin Figure 5, we observe high match scores which sug-gest that color cosmetic lenses increase the inter-classsimilarity.

3. Effect of Lens Detection on Iris Recognition

From the results shown in the previous section, it canbe inferred that contact lens, especially color cosmetic lens,reduces the performance of iris recognition systems. It isour hypothesis that applying a lens detection algorithm to

GenuineMatch

Score: 697

GenuineMatch

Score: 184

GenuineMatch

Score: 0

GenuineMatch

Score: 319

Without Lens Without Lens

Without Lens Transparent Lens

Without Lens Colored Lens

Colored Lens Colored Lens

Colored Lens Colored Lens

ImpostorMatch

Score: 135

Figure 5. Illustrating the effect of contact lenses on iris matchingperformance.

first reject the cases withobfuscatedpatterns and allowingonly without lens (as well as with transparent lens) iris im-ages can potentially improve the performance of iris recog-nition algorithms and reduce the false matches at higher ver-ification rates. To test this hypothesis, we have evaluatedthe performance of four existing techniques: (1) iris edgesharpness [14], (2) textural features based on co-occurrencematrix [14], (3) GLCM based analysis [7], and (4) localbinary pattern (LBP) and SVM based classification. Thefirst three techniques are existing lens detection techniqueswhile the fourth one is a standard texture classification algo-rithm. These lens detection algorithms require segmentediris image as input. Since the commercial systems do notprovide the flexibility of extracting the active contour basedsegmentation of iris regions, we have used the algorithmproposed in [12].

The problem of lens detection in an iris image is ap-proached as a three class classification problem: withoutlens (or normal), transparent lens, and colored lens. How-ever, the iris edge sharpness utilizes thresholding for clas-sification and therefore, the three-class lens classificationfor this approach is converted into a two-class classification

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Colored Remaining Total

Colored 1085 372 1457Remaining 65 231 296Total 1150 603 1753

Table 4. Confusion matrix for colored lens classification problemusing edge sharpness [14].

Normal Remaining Total

Normal 693 370 1063Remaining 479 211 690Total 1172 581 1753

Table 5. Confusion matrix for normal (without lens) classificationproblem using edge sharpness [14].

Normal Colored Transparent Total

Normal 195 157 234 586Colored 80 469 54 603Transparent 184 139 241 564Total 459 765 529 1753

Table 6. Confusion matrix of lens detection using the textural fea-tures [14].

Normal Colored Transparent Total

Normal 192 215 179 586Colored 205 274 124 603Transparent 197 179 188 564Total 594 668 491 1753

Table 7. Confusion matrix for lens detection using the GLCM fea-tures [7].

Normal Colored Transparent Total

Normal 384 37 165 586Colored 30 539 34 603Transparent 297 26 241 564Total 711 602 440 1753

Table 8. Confusion matrix for lens detection using LBP featuresand SVM.

problem. To perform the experiments, images pertaining tothe first 50 subjects are used for training and the remaining51 subjects are used for testing. The classifiers (or param-eters of lens detection algorithms) are first trained on thetraining set and the trained classifiers are used to classifythe input image into one of the three classes. The test setis used to evaluate the performance of the trained classi-fiers on unseen images. With this experimental protocol,Tables 4 to 8 summarize the results of the four lens detec-tion algorithms for images captured using the Cogent sen-sor. Similar confusion matrices are obtained for the Vistasensor as well. These results suggest that colored lenses arerelatively easier to detect compared to transparent lenses.However, differentiating between without lens and transpar-ent lens images is a challenging problem. Further, among

(a) (b)

(c) (d)

Figure 6. Examples of correct (a: without lens and b: colored lens)and incorrect classifications (c: without lens as colored lens and d:colored lens as without lens) using co-occurrence matrix [14].

(a) (b) (c)

Figure 7. Misclassification by LBP and SVM classification: (a)without lens image classified as image with transparent lens, (b)image with transparent lens classified as without lens, and (c) im-age with colored lens classified as image with transparent lens.

all four lens detection algorithms, LBP with SVM classi-fier yields the best classification performance. As shownin confusion matrices (Tables 4 to 8) and examples in Fig-ures 6 and 7, there are several instances when an image withcolored/transparent lens is classified as either without lens(normal) or transparent lens and vice versa. This suggeststhat there is a need for a better lens classification approachthat can delineate different lens classes correctly.

To evaluate the hypothesis that “detecting and reject-ing the iris samples with color cosmetic contact lens canimprove the performance of iris recognition algorithms”,another experiment is performed in which the output oflens classification algorithm is provided as input to theiris recognition system. For this, LBP with SVM classi-fier based lens-detection approach is utilized and the sametraining-testing partitions are used. In this experiment,thegallery contains iris images without lens and the probe con-tains images without lens, with transparent lens, and withcolored lens separately. During probe verification, the im-ages classified as colored lens are declared as “failure toprocess”. Figure 8 shows the ROC curves obtained withthis protocol and its comparison with the results obtainedwhen the gallery image is without lens and all the probeimages are considered without lens classification. It also

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GA

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%)

GalNormal−ProbeDetected Color RemovedGalNormal−ProbeNormalGalNormal−ProbeColoredGalNormal−ProbeTransGalNormal−ProbeComplete

Figure 8. ROC curves demonstrating the effectiveness of incor-porating lens detection algorithm (LBP features and SVM) withVeriEye.

compares the performance when probe is only without anylens (normal), only transparent lens (without classification),and only colored lens (without classification). At 0.01%FAR, normal gallery-probe images yield 99.36% verifica-tion accuracy, whereas with lens classification, all the probeimages yield 72.95% accuracy. By applying lens detectionalgorithm, the accuracy improves to 94.41%. The resultssuggest that the removal of contact lens leads to increase inthe recognition accuracy as compared to without lens clas-sification. However, it is still lower than the accuracy ofnormal-normal and transparent-normal gallery probe pairsdue to less accurate lens detection algorithm.

4. Conclusion and Future Work

Contact lenses, especially color cosmetic lenses, obfus-cate the iris texture and can be viewed as “disguise” for irisbiometrics. This can potentially be an important covariateof iris recognition systems. This paper analyzes the effectofcontact lenses on the performance of iris recognition usingthe IIIT-D Contact Lens Iris database, prepared by the au-thors. The results computed using VeriEye suggest that bothtransparent (prescription) and color cosmetic lens (textured)affect the verification accuracy significantly. Analyzing theexisting lens detection algorithms suggest that, though in-corporating lens detection algorithms may improve the ver-ification performance, designing better and improved lensdetection algorithm is of paramount interest. It is also im-portant to conduct the research in developing contact lensinvariant feature extraction and matching algorithms. It isour assertion that with the availability of the IIIT-D CLIdatabase, other researchers may also undertake research inthese directions.

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