characterization of the human iris spectral reflectance with a multispectral imaging system

Characterization of the human iris spectral reflectancewith a multispectral imaging system

Meritxell Vilaseca,1,* Rita Mercadal,1 Jaume Pujol,1 Monserrat Arjona,1

Marta de Lasarte,1 Rafael Huertas,2 Manuel Melgosa,2 and Francisco H. Imai3

1Centre for Sensors, Instruments and Systems Development (CD6), Technical University of Catalonia (UPC),Rambla Sant Nebridi 10, Terrassa, Barcelona 08222, Spain

2Department of Optics, University of Granada (UGR), Campus Fuentenueva s/n, Granada 18071, Spain3Samsung Information Systems America (SISA), 75 West Plumeria Drive, San Jose, California 95134, USA

*Corresponding author: [email protected]

Received 24 July 2008; accepted 15 September 2008;posted 17 September 2008 (Doc. ID 99364); published 15 October 2008

We present a multispectral system developed and optimized for measurement of the spectral reflectanceand the color of the human iris. We tested several sets of filters as acquisition channels, analyzed dif-ferent reconstruction algorithms, and used different samples as training sets. The results obtained showthat a conventional three-channel color camera (RGB) was enough to reconstruct the analyzed reflec-tances with high accuracy, obtaining averaged color differences of around 2–3 CIEDE2000 units and rootmean square errors of around 0.01. The device developed was used to characterize 100 real irises corre-sponding to 50 subjects, 68 prostheses used in clinical practice, and 17 cosmetic colored contact lenses.© 2008 Optical Society of AmericaOCIS codes: 110.4234, 120.5700, 330.1710, 040.1520, 170.3880.

1. Introduction

The study of the color of the human iris is not a dee-ply explored field so far. The coloration of this struc-ture depends on hereditary factors, such as theamount of melanocyte cells and melanine granulesinside the iris, as well as their distribution [1].The first measurements performed in this area werelimited to simple subjective observations of the eye[2,3]. Recently, some more studies focused on iriscolor measurement using standard color instrumen-tation, such as telespectroradiometers, have ap-peared [4]. However, due to the coloration andtexture variability of the iris, measurement of its col-or becomes a difficult issue because of the limitedspatial resolution commonly present in conventionaldevices. Often, large areas of the iris are integratedand, therefore, only an averaged spectral reflectance

profile or, equivalently, a mean color, can be extractedfrom the collected data. Therefore, the use of systemswith higher spatial resolution to characterize thisstructure is advisable and, in consequence, multi-spectral systems, which commonly consist of a digitalcamera with many pixels, are very appropriate toachieve this purpose.

In this work, based on preliminary studies [5–7],we analyze the feasibility of a conventional multi-spectral system based on a CCD monochromecamera to measure the spectral reflectance in thevisible range associated to the human iris and, there-fore, to characterize its coloration. In the study, theexperimental setup of the multispectral system andthe reconstruction routines used, as well as the train-ing sets, are optimized to achieve high spectral effi-ciency and good color measurement. In this context,the system incorporated several sets of filters used asacquisition channels, which covered the whole visiblerange of the electromagnetic spectrum. According toresearch already carried out [8], in this study two

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5622 APPLIED OPTICS / Vol. 47, No. 30 / 20 October 2008

different filter configurations were studied to achievethe best reconstruction results [9]: on one hand, anRGB tunable filter and an additional blue absorptionfilter were placed in front of the camera, allowing theacquisition of three or six images with only one ortwo exposures, respectively; on the other hand, aset of seven interference filters equispaced alongthe visible range allowed the consecutive measure-ment of seven spectral images. Furthermore, two dif-ferent linear spectral reconstruction methods used toobtain the spectral reflectance of the iris from thecorresponding digital levels were tested: the pseu-doinverse technique [8,10,11] and the principal com-ponent analysis [8,10,12]. These methods allow thereconstruction of spectral reflectances from the cam-era responses by means of an estimation matrix,which can be computed using the known digital out-put levels corresponding to a training set of samples.These techniques also need a priori knowledge of thespectral reflectances associated to the trainingpatches. In this study, several training sets with adifferent size are also tested to perform the recon-structions [8,13].Once the developed multispectral system was op-

timized, it was finally used to characterize the visiblespectral reflectance of 100 real irises correspondingto 50 subjects, 68 prostheses used in clinical practice,and 17 cosmetic colored contact lenses. The recon-structions of the spectral reflectance factors of thethree groups of samples were obtained with differentcombinations of test and training sets. At first, thesame group of samples was used as a test and train-ing set to analyze the feasibility of the system for re-constructing the spectral data. Second, differentgroups of samples were used as test and training setsto study the capability of each group of samples forrepresenting or describing the other two groups, interms of spectral reflectance.By setting up this system, it is possible to perform

analysis of the color and texture of the human iris indetail, as well as the changes that take place in thisocular structure. With the measurements performedusing the system developed, jointly with other colorresearch groups, a representative spectral reflec-tance database associated to the human iris willbe available, which can be useful in some applica-tions, such as in medicine and the cosmetics industry.In these areas, the spectral characterization of theiris may be useful in some medical treatments thatmodify the iris coloration [14,15] and may also allowthe analysis of the pigmentation of this structure[16], which is related to the retinal image quality. Be-cause of the iris translucency, which highly dependson the pigmentation, some scattered light can reachthe retina of the eye, originating retinal straylight.On the other hand, the knowledge of this databasecould make easier the choice of materials and color-ants used to, for instance, manufacture colored con-tact lenses or the artificial eyeballs used asprosthetics. In this context, the use of some specific

colorants can allow obtaining products with a morenatural and realistic appearance.

This paper is structured as follows. In Section 2 theexperimental setup of the multispectral system, thedifferent filter configurations analyzed, and the spec-tral reconstruction algorithms are described, as wellas the samples analyzed. In Section 3, the optimiza-tion of the multispectral system is presented and, fi-nally, the reconstructions of the irises, prostheses,and colored contact lenses are shown. In Section 4,the most relevant conclusions regarding the reflec-tance reconstruction quality and the comparisonsamong groups of samples are described.

2. Methods and Materials

A. Experimental Setup

The multispectral system developed (Fig. 1) consistsof a 12 bit depth cooled CCD monochrome camera(QImaging QICAM Fast1394) with 1.4 megapixels(1392 × 1040), an objective zoom lens (Nikon AFNikkor 28–105mm), and different sets of filters.The two different configurations of filters testedfor the multispectral acquisitions were the following:configuration 1, a color RGB tunable filter attachedto the CCD camera and an additional blue filter[Fig. 2(a)], which permitted obtaining three or siximages by performing only one or two exposures, re-spectively, and configuration 2, a set of seven inter-ference filters placed into a motorized wheel with afull width at half-maximum (FWHM) of approxi-mately 40nm [Fig. 2(b)], which allowed the consecu-tive measurement of seven images with differentassociated spectral information. The supplementaryblue filter used in configuration 1 was chosen, basedon previous study, in order to produce an additionalthree channels. As a result, we have six channels bycombining the trichromatic camera signals with andwithout the absorption filter [17].

Fig. 1. Multispectral experimental setup.

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Furthermore, an illumination system composed ofan adjustable halogen lamp (Philips 15V 150W)attached to a stabilized dc power supply (HewlettPackard 6642A) and a focusing lens, allowing illumi-nation of the analyzed iris with a 45° angle of inci-dence and obtaining a rather uniform luminousfield on the eye. A linear flat-field correction [18]was also applied on the images provided by the com-plete system in order to both correct the camera re-sponse and the nonuniformity of the illumination.Finally, a telespectroradiometer (Photo Research

PR-650) located beside the camera with a reasonablyhigh spatial resolution that is sufficient to see clearlypatterns on the iris was used to measure the spectralradiance associated with very small areas of the iris.To obtain the reflectance values of the samples, a pre-vious measurement of the radiance corresponding toa reference white plate placed at the same position asthe eye was also necessary. The knowledge of the truespectral reflectances of the irises is necessary to trainthe multispectral system and also to compare the re-constructed spectra with real data. The telespectror-adiometer was slightly tilted (18°) with respect to theoptical axis of the multispectral system. This be-comes necessary if simultaneous measurements ofthe iris are required by means of the camera andthe telespectroradiometer due to existing eye move-ments, in order to avoid decentering problems of the

measured zones. Although the measurements withthe camera and the telespectroradiometer were notperformed with identical geometric features, wechecked that this mismatch did not introduce signif-icant errors into the experimental values.

With the described multispectral setup, magnifiedimages with high resolution of the iris of each sub-ject, who leaned on a chinrest with potential horizon-tal displacement, could be obtained through thevarious channels of the CCD and, therefore, it waspossible to obtain the digital signals associated to dif-ferent parts of it. Specifically, we measured the meandigital output levels corresponding to two squareareas of approximately 1mm× 1mm (bottom andright) on the iris (Fig. 3), trying to select those witha rather uniform coloration. The bottom and right po-sitions were selected in order to avoid the region ofthe iris usually covered by the upper eyelid andthe specular reflection of the illumination system,respectively. The corresponding averaged spectral re-flectances in the visible range (380–780nm) of thosezones were also measured with the telespectroradi-ometer. During the acquisitions, the movements ofthe eye can cause the measurement of slightly differ-ent regions on the iris.

As it was mentioned before, two different recon-struction algorithms were tested to estimate thespectral reflectance reconstructions of the irisfrom the digital output levels: the Moore–Penrosepseudoinverse estimation (PSE) and the principalcomponent analysis (PCA). In each of the filter con-figurations described, we used a different number ofchannels to perform the reconstructions: in config-uration 1 we took into account reconstructions per-formed using only three channels (RGB) and sixchannels (adding the blue filter in front of theRGB channels); in configuration 2 we carried out re-constructions using a subset of three interference fil-ters (F450, F550, F650) apart from the complete set,that is, seven. These reconstruction techniques alsoneed a priori knowledge of the spectral reflectancesassociated to the training patches. In this study,we used different training sets to perform the

Fig. 2. (a) Configuration 1, relative spectral sensitivities of theRGB channels of the system (tunable filter and CCD camera)and percentage of transmittance of the additional blue filterand (b) configuration 2, percentage of transmittance of the seveninterference filters.

Fig. 3. Image of an iris and corresponding analyzed areas.


reconstructions, such as the GretagMacbeth Color-Checker color rendition chart (CCCR) and differentsubsets of the measured human irises.To evaluate the accuracy of the spectral estima-

tions of the iris, several parameters [19], such as theroot mean square error (RMSE) and the CIEDE2000color difference [20,21] were used. RMSE valuessmaller than 0.01–0.02 [10] correspond to acceptablespectral estimations. By using the CIEDE2000 for-mula, multispectral systems typically provided colordifferences in the range of approximately 2–3 units[22,23].

B. Analyzed Samples

A group of 52 real human irises corresponding to theright and left eyes of 26 different subjects were usedto optimize the multispectral system. This subset of104 samples (52 × 2 analyzed areas) covered a widerange of iris colorations, such as blue, green, andbrown. Figure 4 shows the spectral reflectances ofthese samples measured with the telespectroradi-ometer PR-650. This delimited number of sampleswas selected for its reduced size, which allowed aneasy manipulation of the data along the optimizationprocess. Specifically, it consisted of 34 brown irisesand 18 blue and green light irises.Once the configuration of the system was opti-

mized in terms of filters and reconstruction algo-rithms using the former group of samples, thedevice was finally checked by using it to estimatethe spectral reflectance of 100 extra irises (54 brownand 46 blue and green) corresponding to 50 subjects(200 samples), a set composed of 68 prostheses pro-vided by Ovidio S. L., (Barcelona, Spain) and used indaily clinical practice (136 samples), and 17 coloredcontact lenses (CIBA VISION Fresh Look) with verydifferent colorations (34 samples). The subject usedto measure the contact lenses was always the sameand had a brown iris. The spectral reflectance pro-files corresponding to these samples measured with

the telespectroradiometer PR-650 can be seenin Fig. 5.

3. Results

A. Optimization of the System

As it was mentioned before, 52 irises were used forthe optimization of the system. These samples weremeasured using the two different filter configura-tions described in Subsection 2.A. Subsequently,the mean digital output levels of the selected zoneswere calculated and the corresponding mean spectralreflectances were also determined by means ofthe telespectroradiometer. After these experimentalmeasurements, we applied the PSE and PCA algo-rithms to estimate the spectral reflectances fromthe digital output levels.

Fig. 4. Spectral reflectance factors of 52 human irises measuredwith the telespectroradiometer PR-650.

Fig. 5. Spectral reflectance factors of the (a) 100 human irises,(b) 68 prostheses, and (c) 17 colored contact lenses measured withthe telespectroradiometer PR-650.


The reconstructions were performed by usingdifferent a priori known training sets: first, the 24color patches of the CCCR were used as a trainingset and the 52 irises (104 samples) were consideredas the test set. Reconstruction results obtained whenthe PSE algorithm and configuration 1 were used canbe seen in Table 1. In this table, the mean and stan-dard deviation of the CIEDE2000 color differenceand the RMSE are shown. As it can be seen, thecolorimetric and spectral accuracy achieved is notgood, meaning that the CCCR is not an appropriaterepresentation of the colors associated to the humaniris tested and that, therefore, it is not useful fortraining the system. It can also be seen that the in-crease of channels still produces bad results due tothe mismatch between the training and test colors.Similar results are obtained with the PCA methodand configuration 2.To improve these results, we also considered the 52

irises as training and test sets, simultaneously. Theresults obtained in this case are shown in Table 2.Now, the color differences obtained and the corre-sponding spectral accuracies are, in general, very sa-tisfactory. With three channels, the results obtainedby means of the PSE algorithm are slightly betterthan when the PCA is used, although the differenceis negligible. However, the results are almost exactlythe same when six or seven channels are used. Be-cause of the easier implementation of PSE, this algo-rithm is preferred. Regarding the set of filters used inthe acquisitions, it can be seen that configuration 1provides slightly better colorimetric parametersthan configuration 2, even though the spectral accu-racy achieved is similar. Because of the similarity ofthe results, configuration 1 is preferred due to itseasier implementation. This configuration only in-volves one or two acquisitions, depending on thenumber of channels desired, while configuration 2consists of seven exposures, which can increasesignificantly the acquisition time and, therefore,can become more uncomfortable for the subject.Furthermore, because of the smaller spectral band-width (FWHM) of the interference filters and theuse of only one focal position for the central channel(F550), the images corresponding to the extremechannels can appear slightly fuzzy. These facts mayintroduce some additional errors in the calculationsand may explain the results found. Finally, the use ofsix channels in configuration 1, that is, the RGBchannels and the extra blue absorption filter, doesnot report a significant improvement of the recon-struction results and the accuracy obtained with

three spectral bands is already acceptable. Thiscan be also understood from Fig. 6, where the cumu-lative proportion index associated to the componentsof the PCA performed over the 52 irises is shown. It ispossible to see that three or four eigenvectors cantheoretically reconstruct very well all the reflectancecurves, which means that the use of three or fourchannels can be enough to obtain good colorimetricand spectral accuracy of the irises. Therefore, itcan be stated that the use of a common RGB imageand the PSE estimation provide good spectral recon-struction for the human irises analyzed, obtaining, inthis case, CIEDE2000 color differences close to 2units, and RMSE values smaller than 0.01.

To take advantage of any multispectral system,it must be trained with a limited set of samples, thatis, using a training set smaller than the test one.Therefore, the 104 samples corresponding to the52 irises were reconstructed again but. in this case.using smaller training sets, specifically with subsetsof the measurements already performed. Because ofthe results previously shown, to perform the recon-structions we only took into account the PSEalgorithm and configuration 1 with three channels.In this context, we used as training sets a subsetof 56 samples corresponding to 28 irises and anotherone composed of 32 samples of 16 irises. In bothcases, the subsets considered included varied reflec-tance curves, which constituted a wide representa-tion of the whole set of irises measured. The resultsobtained are shown in Table 3. As it can be seen, thereconstructions are almost as good as before,with mean CIEDE2000 values close to 2 and RMSEvalues smaller than 0.01; the good reconstructionsstill obtained in terms of color and spectral accuracyusing these subsets could be explained by therelatively small variability of the colors associatedto the human iris. Therefore, the use of only a delim-ited amount of spectral basis curves could be enoughto estimate a great number of different iris samplesand this ensures the robustness of the system de-veloped.

Figure 7 shows the CIEDE2000 histogram corre-sponding to the 52 irises, that is, the 104 measuredand estimated reflectance values using the trainingsets described. As it can be seen, in reducing thetraining set size, the distribution of values in theshown intervals changes. The smaller the trainingset is, the greater differences appear, specifically,above 3 units, although the mean results can stillbe considered very acceptable.

Finally, the reflectance reconstructions have alsobeen performed taking into account the color gamutof the measured eyes, that is, separating the darkirises (brown irises) from the light ones (blue andgreen irises). This has been done to try to improvethe former obtained results in terms of color andspectral accuracy. The reconstructions have beenfirst developed using the spectral reflectances corre-sponding to the 34 brown irises as training and testsets and, second, with the spectral profiles of the 18

Table 1. Color Difference and Root Mean Square Error (Mean �St. Dev.) for the 52 Human Irisesa

Configuration 1 CIEDE2000 RMSE

3 channels 22:37� 3:84 0:1736� 0:05796 channels 19:77� 6:28 0:0755� 0:0422

aTraining, 24 CCCR; test, 52 human irises; method, PSE; con-figuration, 1.


blue–green irises also included in the 52 irises data-base. As before, the reconstructions were carried outwith the PSE algorithm and configuration 1 withthree channels. The results obtained for the two ana-lyzed groups are shown in Table 4. It can be seen thatthe reconstructions for the brown irises are slightlybetter in this case, that is, when only the dark sam-ples are included in the training set. The meanCIEDE2000 value is now slightly smaller than 2(1.97) and the RMSE parameter is 0.0065. However,this only represents a small improvement with re-spect to the results obtained when the completeset of 52 irises was used as a training and a testset (CIEDE2000 ¼ 2:09, RMSE ¼ 0:0072). Further-more, in the case of the blue–green irises, the resultsare slightly worse, with a 2.12 CIEDE2000 and a0.0084 RMSE mean values. Again, this only repre-sents a very small change with respect to the recon-structions achieved with the complete set of humanirises. Therefore, in the following reconstructions wewill not separate the samples by taking into accountthe color gamut because, with the use of this meth-odology, a clear improvement of the spectral resultsis not reported.

B. Spectral Estimations

As it was previously mentioned, to test the developedsystem it has been finally tested over 100 real humanirises (200 samples), 68 prostheses (136 samples),and 17 colored contact lenses (34 samples). First ofall, the PCA was performed over them. The corre-sponding cumulative proportion indexes (%), thatis, the percentages explained by different numbers

of eigenvectors for the different groups are listedin Table 5. As it can be seen, for the three groupsof samples, the three first principal components ex-plain more than 99% of their variance. This is inagreement with the results obtained in the optimiza-tion of the system, where it was found that threechannels (RGB) were enough to obtain good recon-structions of the spectral reflectances associated tothe human iris.

The reconstructions of the spectral reflectances ofthe three groups of samples considered were first as-sessed using the same groups as training and testsets. The corresponding results are shown in Table 6.The three groups analyzed have mean color differ-ences of around 2 CIEDE2000 units or even smaller,meaning that the reconstructions in terms of colordifferences are accurate in terms of multispectralimaging systems. The same conclusion is obtainedfrom the RMSE parameter. In this case, averagedvalues similar to 0.01 are obtained, which mean goodreconstructions of the visible reflectance. Further-more, it can be seen that the best results are obtainedfor the prostheses. This can be explained by takinginto account experimental errors associated withthe measurements: the artificial eyeballs do not pre-sent any of the movement associated with the othertwo types of samples, whereas, when the human sub-ject’s eye is part of the scene, this is not true. Thisleads to a less precise measurement in the last cases.Figure 8 shows some representative examples ofspectral reflectance reconstructions for the humanirises, the prostheses, and the contact lenses. Speci-fically, the best and the worst reconstructions foreach group can be seen. From the results presentedabove, it can be affirmed that the system has the cap-ability of providing accurate reconstructions of thevisible spectral reflectances of the samples.

Once the feasibility of the system has been tried forthe last three groups of samples individually, thenext step is answering whether the system can betrained with a different set of samples regarding

Table 2. Color Difference and Root Mean Square Error (Mean � St. Dev.) for the 52 Human Irisesa

PSE PCA

Configuration 1 CIEDE2000 RMSE CIEDE2000 RMSE (×100)

3 channels 2:09� 1:05 0:0072� 0:0045 2:69� 1:06 0:0077� 0:00446 channels 2:06� 0:95 0:0069� 0:0042 2:06� 0:95 0:0069� 0:0042Configuration 23 channels 2:67� 1:44 0:0072� 0:0040 3:41� 1:47 0:0077� 0:00397 channels 2:31� 1:26 0:0065� 0:0038 2:31� 1:25 0:0065� 0:0038

aTraining, 52 human irises; test, 52 human irises.

Fig. 6. Cumulative proportion index (%), that is, percentage ex-plained by different numbers of principal components of the PCAanalysis performed on the subset of 52 irises (104 samples).

Table 3. Color Difference and Root Mean Square Error (Mean� St. Dev.) for the 52 Human Irisesa

Training CIEDE2000 RMSE

28 irises 2:16� 1:15 0:0074� 0:004716 irises 2:20� 1:12 0:0079� 0:0049

aTest, 52 human irises; method, PSE; configuration, 1, withthree channels.


the actual test set. This will inform us about the cap-ability of each group of samples of describing thespectral reflectance profiles of the other two groups.The reconstruction results obtained when the train-ing and test sets correspond to different groups ofsamples are shown in Table 7. It can be seen that,when the system is trained using the human irises,the reconstructions for the prostheses and the con-tact lenses are still rather accurate, meaning thatthe 100 irises are a quite good representation ofall the colors associated to the different samples ana-lyzed. In this case, the mean color differences arearound 2–3 CIEDE2000 units and the RMSE isaround 0.01, values for which the reconstructionscan be still considered accurate. Better results areobtained for the contact lenses, probably becausethe measurements of these samples are performedon a real iris and, therefore, the actual reflectancemeasured is mainly affected by the contact lens,but also by the color of the subject’s iris underneath.On the other hand, when the system is trained usingthe prostheses and the reflectances belonging to theother two groups of samples are reconstructed, theresults are worse. The mean CIEDE2000 in thesecases are close to 4 and the RMSEs are slightly high-er. This implies that the reflectances associated tothe 68 artificial eyeballs do not represent the othersamples with as much precision as the real irisesdo. This could be because of the colorings and pig-ments present in this kind of samples, as well asthe texture, features which can differ from the real

colors of the iris. The same explanation could be ap-plied to the contact lenses, because, as it has beenstated before, the color measured on them is also af-fected by the real iris under them.

Finally, if the colored contact lenses are used as thetraining set, the results of reconstructing the humanirises are similar to those obtained when the systemis trained using the prostheses, while the results ofreconstructing the prostheses are, in general, poor. Inthese cases, the mean CIEDE2000 is close to 4 and 6,respectively, and, in both cases, the standard devia-tion associated is very high, meaning that there is ahigh dispersion among the results. This can also beseen from the corresponding ranges, that is, themini-mum and maximum color difference and RMSE va-lues. Probably this can be due to the limited numberof contact lenses analyzed, as well as their reflec-tance profiles, which cannot be representative of thereal reflectances present in human eyes and the ar-tificial eyeballs.

4. Conclusions

We showed the feasibility of a multispectral systemto be used as a tool to estimate the spectral reflec-tance associated to the human iris. The system devel-oped, which basically consists of a CCD camera, anobjective zoom lens with great magnification, severalsets of filters, and a halogen lamp used to light theanalyzed iris, was optimized for this purpose. Thesystem allowed performing a spectral reflectance

Fig. 7. CIEDE2000 histogram for the 52 human irises obtainedusing three channels (test, 52 human irises; method, PSE; config-uration, 1).

Table 4. Color Difference and Root Mean Square Error (Mean �St. Dev.) for the 34 Brown Irises and the 18 Blue–Green Irisesa

CIEDE2000 RMSE

Brown eyes 1:97� 0:92 0:0065� 0:0030Blue-green eyes 2:12� 1:01 0:0084� 0:0055

aTraining, 34 brown irises/18 brown irises, respectively; test, 34brown irises/18 brown irises, respectively; method, PSE; configura-tion, 1, with three channels.

Table 5. Cumulative Proportion Index (%) for the First ThreePrincipal Components (PC) Corresponding to the 100 Human

Irises, 68 Prostheses, and 17 Colored Contact Lenses

PC Irises Prostheses Contact Lenses

1 64.71 68.85 51.712 95.82 97.35 94.673 99.21 99.08 99.01

Table 6. Color Difference and Root Mean Square Error (Mean,Standard Deviation, Minimum, and Maximum Values) for the100 Human Irises, 68 Prostheses, and 17 Colored Contact

Lensesa

CIEDE2000 RMSE

Training set ¼ test set, human irisesMean 2.46 0.0108St. Dev. 1.10 6.4739e-3Min. 0.50 2.3433e-3Max. 5.44 0.0345Training set ¼ test set, prosthesesMean 1.97 0.0111St. Dev. 0.91 6.2001e-3Min. 0.46 2.4249e-3Max. 4.44 0.0342Training set ¼ test set, contact lensesMean 2.57 0.0105St. Dev. 0.95 6.1035e-3Min. 0.77 2.5625e-3Max. 4.01 0.0229

aTraining set ¼ test set; method, PSE; configuration, 1, withthree channels.


estimation of the iris by acquiring only some imagesof the eye and applying linear algorithms, such as thePSE and the PCA. We evaluated different configura-tions of the system in order to determine the one thatprovides the best spectral results and has the easiestimplementation. This was accomplished by using aconfiguration composed of a RGB tunable filter,which allowed obtaining three monochrome imagesin sequence. The application of the PSE and thePCA algorithms led to similar results, although thePSE was preferred because of its simplicity. We alsotested different training sets for the system andfound that a reduced subset of real iris samplescan be enough to obtain acceptable results of reflec-tance reconstruction.With the optimized system, the spectral reflec-

tance curves in the visible spectrum of three groupsof samples were analyzed. The groups consisted ofreal human irises, clinical prostheses, and coloredcontact lenses. The reconstructed spectral profilesshowed a high level of spectral and color accuracyand color, mainly when the system was trained bymeans of the human iris database. In general, themean CIEDE2000 color differences obtained wereclose to 2–3 units and the RMSE parameter was si-milar to 0.01.

We showed the possibility of estimating thespectral reflectances of irises from simple RGBimages. The use of this system will allow character-izing the color of human irises with high resolutionand creating a wide database of spectral reflectances.This can be useful in some medical applications, forinstance, in the determination of the pigmentation of

Table 7. Color Difference and Root Mean Square Error (Mean,Standard Deviation, Minimum, and Maximum Values) for the 100Human Irises, 68 Prostheses, and 17 Colored Contact Lenses

CIEDE2000 RMSE

Training set, human irises; test set, prosthesesMean 2.90 0.0179St. Dev. 1.22 0.0115Min. 0.57 3.0910e-3Max. 5.97 0.0569Training set, human irises; test set, contact lensesMean 2.42 0.0129St. Dev. 1.09 6.8951e-3Min. 0.79 2.3770e-3Max. 5.48 0.0316Training set, prostheses; test set, human irisesMean 3.78 0.0165St. Dev. 1.23 8.4533e-3Min. 0.55 4.6276e-3Max. 7.58 0.0569Training set, prostheses; test set, contact lensesMean 4.04 0.0182St. Dev. 1.05 7.0793e-3Min. 1.82 9.6825e-3Max. 6.78 0.0390Training set, contact lenses; test set, human irisesMean 3.79 0.0167St. Dev. 1.77 9.0554e-3Min. 0.47 1.9612e-3Max. 8.43 0.0458Training set, contact lenses; test set, prosthesesMean 5.86 0.0250St. Dev. 2.34 0.0118Min. 1.30 5.3829e-3Max. 11.38 0.0573aTraining set ≠ test set; method, PSE; configuration, 1, with

three channels.

Fig. 8. Best and worst spectral reflectance reconstruction for thethree groups of samples analyzed: (a) human irises, (b) prostheses,and (c) colored contact lenses. Solid curves indicate the recon-structed spectral reflectances and dashed curves indicate the mea-sured (PR-650 telespectroradiometer) ones.


the iris, which can affect the retinal image quality, aswell as in the observation of color changes over timecaused by some treatments. In the cosmetics indus-try, the spectral characterization of the iris can beuseful in prostheses manufacturing, as well as inthe contact lenses industry. This knowledge can leadto more a natural and realistic appearance of themanufactured products.

This research was supported by the Spanish Min-istry for Science and Technology (Ministerio de Cien-cia y Tecnología) by means of grant DPI2005-08999-C02-01. M. de Lasarte would like to thank the Min-isterio de Educación, Ciencia y Deportes of Spain forthe Ph.D. grant she has received. Authors from theUniversity of Granada acknowledge research projectFIS2007-64266, Ministerio de Educación y Ciencia(Spain), with Fondo Europeo de Desarrollo Regional(FEDER) support.

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characterization of the human iris spectral reflectance with a multispectral imaging system

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