1

NISTIR 7828

Ocular and Iris Recognition 

Baseline Algorithm

Yooyoung Lee 

Ross J. Micheals

James J. Filliben 

P. Jonathon Phillips

Hassan A. Sahibzada
2

NISTIR 7828

November 2011

U.S. Department of Commerce

Rebecca M. Blank, Acting Secretary

National Institute of Standards and Technology

Patrick D. Gallagher, Under Secretary for Standards and Technology and Director

Ocular and Iris Recognition 

Baseline Algorithm

Yooyoung Lee

Ross J. Micheals

P. Jonathon Phillips

Hassan A. Sahibzada

Information Access Division 

Information Technology Laboratory

James J. Filliben

Statistical Engineering Division 

Information Technology Laboratory

National Institute of Standards and Technology 

Gaithersburg, MD 20899
Abstract 

Due to its distinctiveness, the human eye is a popular 

biometric feature used to identity a person with high 

accuracy. The “Grand Challenge” in biometrics is to have 

an effective algorithm for subject verification or 

identification under a broad range of image and 

environmental conditions. As a response to the challenge, 

this paper presents baseline performance results derived 

from an enhanced version of VASIR (Video-based 

Automated System for Iris Recognition), as well as initial 

performance results based on a broader ocular recognition 

system. We describe the details of the VASIR procedure and 

demonstrate its superiority over the IrisBEE (Iris Biometric 

Evaluation Environment) baseline algorithm. We examine 

the relationship between VASIR performance and image 

quality scores. Finally, for less-constrained imaging 

conditions, we provide a comparison of iris and ocular 

recognition results. 

1. Introduction 

Human physiological or behavioral characteristics can 

be integrated into a system to identify a person; such a 

system is called a Biometric system. Specifically, the 

human eye is a highly distinctive feature that can provide a 

promising security application which we call iris 

recognition. 

Iris recognition is a method to identify a person which 

focuses on and makes use of the distinctive and unique 

patterns of the human iris. 

Ocular recognition is a multi-components method which 

makes use of a variety of features in and around the human 

eye: eyebrows, eyelashes, eyelids, eye shape, sclera, iris, 

pupil, etc. Iris recognition makes use of some of the same 

multiple components, but only as a means to an end—to 

navigate and to extract the iris itself, from which all 

subsequent analytic comparisons are done. On the other 

hand, ocular recognition treats these components as 

features onto themselves which become part of the 

comparative process. 

A challenge of current iris recognition systems is often 

limited by significant input and environmental restrictions 

related to image acquisition. For example, an iris 

recognition system might perform well only for ideal 

imaging conditions which were captured with devices 

specially designed for the system. 

With more factors at its disposal, ocular recognition has 

the potential for higher comparative power—bringing that 

potential to a practical reality is the challenge that this 

paper addresses. 

The broad goal of this paper is to encourage the 

development of robust ocular/iris recognition algorithms 

that yield accurate biometric results for a broad range of 

image and environmental conditions; e.g., a near-infrared 

(NIR) video- frame captured at a distance with an Iris On 

the Move (IOM) 1 system which we call “distantimage-based” contrasted with a classical image acquisition 

system such as the LG 2200 EOU which we call 

“classic-image-based” iris recognition. 

Ocular/iris recognition under challenging imaging 

conditions is a relatively new research subject which still 

needs to overcome a number of issues. The main purpose of 

this paper is to provide a performance reference for 

ocular/iris recognition to address current problems. 

As part of the description of ocular systems, this paper 

describes a particular iris recognition system VASIR 

(Video-based Automated System for Iris Recognition) [1] 

which was developed to address, via a rigorous and 

structured design and analytic methodology, such image 

and environmental challenges. 

To establish additional robustness in the VASIR system, 

we present a modified Hamming distance (HD) matching 

methodology for improving False Reject Rates (FRR). 

To assess an ocular/iris system’s performance, we follow 

the same framework that was used for the VASIR project: 

1) Is VASIR robustly superior to IrisBEE? 

2) Can VASIR’s results be used as a state-of-the-art 

baseline for both classic-image-based and distantimage-based ocular/iris recognition? 

3) How does VASIR performance change with the 

image quality? 

1 The identification of any commercial product or trade name does not imply endorsement 

or recommendation by the National Institute of Standards and Technology.

Ocular and Iris Recognition Baseline Algorithm 

Yooyoung Lee, Ross J. Micheals, James J. Filliben, P. Jonathon Phillips, Hassan A. Sahibzada 

NIST/ITL, 100 Bureau Drive, Gaithersburg, MD 20899, USA 

{yooyoung, rossm, filliben, jonathon, hassan}@nist.gov 
The VASIR system was evaluated using ICE (Iris 

Challenge Evaluation) 2005, MBGC (Multiple Biometrics 

Grand Challenge), and FOCS (Face and Ocular Challenge 

Series)—subset from MBGC—datasets provided by the 

National Institute of Standards and Technology (NIST) 

[2][3]. 

Because of the existing test methodology developed in 

the VASIR project, the paper as a whole will be structured 

so as to present VASIR iris results first followed by 

corresponding ocular results. 

Section 2 gives a brief overview of IrisBEE and VASIR. 

Section 3 discusses a new matching method. Section 4 

introduces ocular recognition. Section 5 describes the ICE, 

MBGC, and FOCS datasets. Section 6 describes detailed 

performance results vis-à-vis the three above-mentioned 

questions. 

2. IrisBEE and VASIR 

Most iris-based systems currently in existence are for 

controlled classic-image-based iris recognition. IrisBEE, 

based on Masek’s algorithm [2][4], was originally 

developed at NIST to be used as a reference 

implementation for such a classic-image-based iris 

recognition. 

In the acquisition phase of IrisBEE, an image of the 

subject’s eye region is taken through classical image 

acquisition system (e.g., LG2200 EOU)—such an image is 

captured with careful image quality controls. 

In contrast to the IrisBEE’s algorithm, VASIR was 

developed with less constrained video-based iris 

recognition in mind. VASIR also robustly allows 

performing classic-image to classic-image (CC), distantimage to classic-image (DC), and distant-image to 

distant-image (DD) iris recognition. In CC case, a 

classic-image is matched against other classic-images of 

the same person that were captured by the same device 

(LG2200 EOU system). DC means the video-frame 

captured at a distance (IOM system) is compared to 

classic-images, captured by a different camera. In DD case, 

the extracted iris region of distant-images is matched to 

other frames either from the same video or different video 

sequences of the same person. 

Figure 1 illustrates the procedural components of 

VASIR. The VASIR image acquisition step includes a 

facial-visible NIR video captured while a person walks 

through a portal, in addition to a classical still iris image. 

Four additional steps are used to identify a person from less 

constrained video at a distance. First, VASIR detects the 

eye region within each frame. Second, the system 

automatically extracts an eye region sub-image. Then the 

algorithm measures the quality of the detected eye region 

sub-images by calculating the edge density within the 

images. Finally, VASIR selects the best quality image out 

of all frames—a procedure we call Automated Image 

Quality Measurement (AIQM) [1]. 

Figure 1: VASIR procedure 

To facilitate comparing two iris images, Daugman’s 

polar coordinate-based system [5] is then employed to 

normalize the iris region. The 1D-Log-Gabor Filter is used 

to process the feature extracting and feature encoding of the 

normalized iris images, as introduced by Yao et al [6]. 

VASIR was developed with the normalization and feature 

extraction based on Masek’s algorithm [4]. 

3. Template Matching 

The Hamming distance (HD) counts the number of 

positions (in two templates of the same size) for which the 

binary digits are different. The HD can be used to decide 

whether or not two iris templates are of the same person. A 

noise mask [4] helps to exclude the insignificant bits of a 

template using the equation: 









 

  

 N

k

k k

i i

N

i

i i

N Tm Qm

T Q Tm Qm

HD

1

1

( )

( ) ( )

, where T and Q are two bit-wise templates and Tm and 

Qm are the corresponding noise masks. N is the total bits of 

a template. 

For rotational inconsistencies between two iris 

templates, one template is two bit-wise shifted left or right 

and the HD value is selected from successive shifts [11]. 

However, this method may lead to matching failure 

because the pupil and iris may not be segmented to the 

exact circle boundaries—especially when the image has 

significant noise from motion blur or inconsistent lighting 

conditions. In reality, the iris outer boundary is not 

uniquely-defined due to its gradual gradient change from 
the iris to the sclera. Figure 2 illustrates a case of 

less-than-optimal segmentation of the pupil (a) and (b) iris 

boundaries. 

Figure 2 Example of inaccurate segmentation (a) pupil and (b) iris 

We therefore propose a new matching method in which 

the template is shifted not only left and right (horizontal) 

bit-wise, but also upward and downward (vertical); the 

Hamming distance values for these X and Y shifts are 

indicated by HD_X and HD_Y, respectively. Figure 3 

shows the detailed HD shifting method. 

Further, we select successive shift from the values of X 

or Y (HD_XorY). Finally, the template can also shift in two 

directions at the same time—HD_XandY. For instance, 

when one template shifts by two bits to the left, it can also 

shift one bit up. The new matching method can help to 

cover both the rotational inconsistency and the 

segmentation inaccuracy. 

Figure 3 VASIR matching method 

For all three scenario cases of a) classic-image to 

classic-image (CC) b) distant-image to classic-image (DC), 

and c) distant-image to distant-image (DD) in Figure 4 

illustrates the comparison of the HD shifting methods for 

both the left and right eye based on the MBGC dataset. 

The four types of HD shifting methods (HD_X, HD_Y, 

HD_XorY and HD_XandY) were evaluated with the 

following criteria for the comparison; mean of False Reject 

Rates (FRRs) taking at False Accept Rate (FAR) of 0.0001, 

0.001, 0.01, 0.1, and 0.999 (left and right)—for which 

smaller is better. 

For CC case, the matching scores are ordered by XorY, 

XandY, X, and then Y methods. XorY also leads with the 

best results followed by the Y, X, and XandY for DC. 

Interestingly, Y is better for DD, (opposite to the results of 

CC) and then XorY, X, XandY follow in order. Overall, 

XorY is better because XorY is best or second best in all 

three scenario cases. 

4. Ocular Recognition 

For ocular recognition, we took the high resolution NIR 

distant-images and used the iris location to extract an ocular 

region surrounding the eye of size 750 × 600 pixels. For 

comparison with HD_XorY, we performed direct cosine 

correlation—described in below—on the ocular regions to 

attain a similarity score; 

∑ ܶ௜ ൈ ܳ௜ ୬

୧ୀଵ

ඥሺ∑ ሺܶ௜ሻଶ 	ൈ	∑ ሺܳ௜ሻ ୬ ଶ ୧ୀଵ ୬

୧ୀଵ

 The ocular region includes the entire eye, eyelashes, part 

of the nose, and sometimes the eyebrow. In addition we 

provide results from a fusion algorithm where we 

normalize and combine the scores from cosine correlation 

and HD_XorY to produce a similarity score for each pair of 

ocular regions. 

By fusing the results from the iris alone and the entire 

ocular region, we examine whether we can better perform 

classification with the aid of the extra information 

contained in the ocular region as compared to the iris alone. 

If a fusion algorithm performs better than either the ocular 

Figure 4 Comparison of four HD shifting methods for each of three scenario cases; plotted by FRRs’ mean value taking at FAR

of 0.0001, 0.001, 0.01, 0.1, and 0.999; (a) classic-image to classic-image (CC) (b) distant-image to classic-image (DC) 

(c) distant-image to distant-image (DD) 
or iris algorithm by itself, then one would expect that the 

ocular region provides us with something extra that we 

don’t get with the iris alone. 

Figure 5 Differences between (a-b) distant-image captured by a 

IOM and (c-d) classic-image captured by a LG2200 EOU; 

(a) and (c) are from person A, (b) and (d) are from person B 

5. Datasets 

5.1. Iris Challenge Evaluation (ICE) 2005 

The ICE 2005 data was collected at the University of 

Notre Dame in cooperation with NIST. The near-infrared 

iris still images are 640x480 in resolution and were 

captured by a LG EOU 2200 acquisition system. 

Table 1 ICE2005 dataset (NIR_Iris_Still) 

Position No. of Images No. of Subjects 

Left Eye 1,528 120 

Right Eye 1,425 124 

The total number of iris images is 2,953 and only 112 

subjects exist for both right and left out of the total 132 

subjects. In the original ICE2005 dataset, the image 

“246260.tiff” was mistakenly included in the left image set 

instead of in the right images [2], and consequently was set 

aside in our study. 

5.2. MBGC (Multiple Biometrics Grand Challenge) 

and FOCS (Face and Ocular Challenge Series)

Our VASIR performance study used two different data 

types included in the MBGC dataset. 

The near-infrared facial video (NIR_Face_Video or 

distant-image) were captured by an Iris On the Move 

(IOM) system; each frame is 2048x2048 pixels and the iris 

diameter is ~100 pixels. The near-infrared iris still images 

(NIR_Iris_Still or classic-image) were collected by an 

LG2200 EOU and are 640x480 in resolution and the iris 

diameter is ~200 pixels [12]. 

Table 2 MBGC Datasets 

IOM System No. LG2200 EOU No. 

Total video 628 Left 4,025

Both eyes visible 598 Right 4,013

Total frames 11,341 

The detailed numbers for distant (IOM) and classic 

(LG2200) iris images can be found in Table 2. 

Figure 5 shows the differences between distant-image 

and classic-image in the MBGC dataset. 

FOCS dataset is a subset of the MBGC dataset, which 

contains 9,592 images (left and right); each image is 

750x600 in resolution. 

6. Experiments 

In this section, we describe which performance metrics 

are applied and how our different experiments are designed 

and executed to evaluate VASIR performance. 

Biometric systems are conventionally evaluated by the 

True Accept Rate (TAR)— known as Verification Rate 

(VR)—where two biometric samples are correctly decided 

to be the same person and the True Reject Rate (TRR) 

where the system correctly decided that two samples 

(genuine and imposter) are not the same person. The False 

Accept Rate (FAR) is defined as the rate where people are 

accepted as the same identity while in reality they are 

different and False Reject Rate (FRR) is where two samples 

from the same person are rejected. The TRR and FRR can 

be computed as FRR=1-TAR and TRR=1-FAR, 

respectively. The TAR and FAR are normally obtained by 

varying an acceptance decision threshold . 

The Equal Error Rate (EER) is defined as the rate where 

FAR( ) and FRR( ) are the same value (i.e., intersect on 

a FAR and FRR vs.  plot). The Receiver Operator 

Characteristic (ROC) curve is a plot of TAR against FAR. 

In terms of Verification (1:1), an image matches if its 

matching score has satisfied a certain threshold , whereas 

Identification (1:Many) normally indicates that an image 

matches if its score is the best. Our results are based on 

verification. 
Figure 6 Comparison of VASIR and IrisBEE for the left and right eye (classic-image to classic-image matching) using ICE2005 dataset

6.1. Comparison of VASIR and IrisBEE 

Is VASIR robustly superior to IrisBEE? In order to 

answer this question, the ICE 2005 evaluation protocol [2] 

was applied to measure VASIR’s performance for 

comparison purposes. An algorithm compares a query 

image Qi to a target image Ti to produce the similarity 

scores for matching and non-matching. The two criteria for 

the comparison are: 

1) Verification Rate (VR) at False Accept Rate (FAR) of 

0.001 (for which higher is better) and 

2) Equal Error Rate (EER) value—for which smaller is 

deemed as better. 

For this experiment, only classic-image to classic-image 

(CC) matching protocol is allowed since the ICE 2005 

dataset were collected by a LG EOU 2200 system. Note 

that the images for the subjects were captured on different 

days. 

Figure 6 shows the comparison between IrisBee's and 

VASIR's results by plotting the ROC curve. For the left 

eye, at FAR of 0.001, VASIR has a VR of 88.5% while 

IrisBEE is only 85.0%. For the right eye, VASIR’s VR is 

90.3% while IrisBEE’s VR is 85.2%. Hence for both eyes, 

VASIR is an improvement over IrisBEE. For the EER 

criterion – (not shown), VASIR’s EER is better with 

~4.4%, while IrisBEE’s EER is nearly twice larger. 

The results show that even though VASIR was 

developed primarily as a tool for distant-image-based iris 

recognition, the system is robust for classic-image-based 

iris recognition as well. 

6.2. Baseline for Ocular/Iris Recognition 

Can VASIR’s results be used as a state-of-the-art 

baseline for both classic-image-based and distantimage-based ocular/iris recognition? We present the 

performance results based on the MBGC/FOCS dataset 

using VASIR for the purpose of providing a baseline both 

under less constrained distant-image-based and constrained 

classic-image-based ocular/iris recognition. They can be 

used as a reference for subsequent evaluation or for 

comparison with custom algorithms. These results may 

also provide an opportunity for other researchers to 

advance their ocular/iris recognition algorithms and to 

promote their technology. 

For distant-image data, no eye region could be detected 

in 41 images—out of the total 9,592 images (false detection 

rate: 0.43%). For classic-image dataset, 4,025 images for 

the left and 4,013 images for the right eye were evaluated. 

Furthermore, we provide a prototype of our experiment 

design showing how to evaluate performance results. 

First, VASIR allows us to match: 

1) classic-image to classic-image (CC) 

2) distant-image to classic-image (DC), and 

3) distant-image to distant-image (DD)—all-frame 

against all-frame without selecting the best quality 

image. 

For non-matching scores, the query Qi is compared to all 

targets Ti where the target subject is a different person. This 

full matching (one to all others) can be time- consuming 

and is not necessary since the non-matching scores are 

sufficiently consistent for virtually all query subjects. We 

therefore suggest randomly selecting a subsample of 

targets, all of a different person for getting the 

non-matching results; we found 50 targets to be a sufficient 

subsample. This not only reduces time cost but also allows 

us to characterize the performance for analysis purposes 

more easily. Our results show that in comparison full 

non-matching and random non-matching are nearly 

equivalent. 

In Figure 7 (plots of the left and right ROC curves), we 

compare the performance results of CC, DC, and DD 

matching cases using HD_XorY matching method based 

on the results as shown in Figure 4. 
Figure 7 Comparison of CC, DC, and DD for the left and right 

Figure 8 Comparisons between Iris, Ocular, and Fusion 

The detailed performance rates are described in Table 3. 

The results show that CC matching has the best 

performance; DC and DD follow next as expected. 

Table 3 VASIR’s Performance results (VR at FAR = .001) 

Datasets VR at FAR = 0.001 EER LEFT RIGHT 

CC 0.837 0.847 0.067 

DC 0.105 0.168 0.309 

DD 0.118 0.106 0.326 

Figure 8 shows the ROCs using the Iris, Ocular and 

Fusion algorithms on the NIR distant-imager. It is 

interesting to note that just cosine correlation on the ocular 

regions performs better for recognition purposes as 

compared to the iris recognition using the HD_XorY 

distance measure. 

Table 4 Ocular and Iris Comparison for DD (VR at FAR = .001) 

Method VR at FAR = 0.001 for DD case 

LEFT RIGHT 

Fusion 0.289 0.283 

Ocular 0.203 0.214 

Iris 0.118 0.106 

6.3. Image Quality Scores and Performance 

How does VASIR performance change with image 

quality? Although several metrics[13][14] were used to 

address one or another aspect of image quality metrics, the 

edge density—as defined by the Sobel operator—was our 

primary image quality metric of choices. After first using 

the edge density metric to determine focus, the metric was 

again applied to arrive at a final order categorization of the 

quality of specific images. 

The Sobel operator is designed to respond to edges 

running vertically and horizontally relative to the pixel 
grid. The Dx and Dy are applied separately to the input 

image to measure the gradient component in each 

orientation. The gradient magnitude is given by the 

following equation: 

2 2 QS  Dx  Dy

,which a higher score is considered better image quality 

in our evaluation. 

 Quality score by Flynn et al [10, 15]—obtained by 

measuring a pair of samples q and t from the ICE2006—is 

defined as, 

QS(q,t)  min(QS(q),QS(t))

To calculate a quality measure QS(q,t) for a set of match 

scores M and a set of non-match scores N, decision 

threshold  and image quality threshold  are given as 

parameters. Therefore, VR( , ) is computed as, 

 

 

   

 

     # ( , )# ( , ) ( , ) ( , ) M QS q tM SS q t QS q t VR

M is the set of match similarity scores and SS(q,t) is the 

similarity score where the two images q and t are of the 

same person. The VR is the fraction of similarity score 

SS(q,t) greater than or equal to a decision threshold  , 

where the quality score QS(q,t) is greater than or equal to a 

threshold . 

Furthermore, FAR( , ) is defined as, 

 

 

   

 

     # ( , )# ( , ) ( , ) ( , ) N QS q tN SS q t QS q t FAR

N is the set of non-match similarity scores and SS(q,t) is 

the similarity score where the two images q and t are of the 

different persons. The FAR is the fraction of similarity 

score SS(q,t) greater than or equal to a decision threshold

where the quality score QS(q,t) is greater than or equal to a 

threshold . 

Figure 9 Distribution of quality score (edge density) and 

VASIR-based Hamming distance (XorY) on matching and 

non-matching for DD case 

Figure 9 shows the distribution of the quality scores and 

VASIR-based Hamming distances (XorY) based on left 

and right images for distant-image to distant-image (DD). 

The plot illustrates that the DD dataset consists of poor 

quality images; the scatter points are primarily found on the 

left side of the plot. The Hamming distance of 

non-matching scores is mainly found in the top quarter of 

the plot. The results show that VASIR’s performance and 

image quality scores (edge density) are correlated with the 

coefficient value -0.52 on DD matching, while the 

non-matching has only -0.11. 

Following, the False Reject Rate (FRR) is calculated at 

six levels of the image quality. The quality score for DD 

case is calculated with the edge density ranges 

approximately from 10 to 35. 

Figure 10 VASIR’s performance on the image quality for distant-video to distant-video (DD) matching 
We define a set of six quality levels (0 to 5) with 

thresholds ሼτ଴,...,τହሽ that were uniformly divided; Quality 

level 0 (Q0) is all images; Quality level 1 (Q1) means all 

images with quality score൒ τଵ; the remaining levels are 

Q2 ൒ ߬ଶ, Q3 ൒ ߬ଷ, Q4 ൒ ߬ସ and Q5 ൒ ߬ହ (best). For each 

quality level, VASIR’s performance is evaluated with an 

FRR value with FAR at 0.01, using only samples of all 

quality levels above the current level. Figure 10 illustrates 

the DD performance results of the eye images with the 

matching method by applying HD_XorY. 

The plots in Figure 10 show that the FRR in the 

performance of DD case is reduced for both left and right 

when low quality images are excluded. It is important to 

note that the image quality was measured from the datasets 

“as-is” without localizing the iris region in an image. A 

possible problem is that the quality score will be high if the 

image includes a lot of eyebrows and eyelashes. 

The positive side of these results is that the image quality 

(as measured via the edge density) may serve as a predictor 

of VASIR performance on DD matching. However, there is 

a need for analyzing quality metrics for good image quality 

datasets such as classic-image iris images. We are pursuing 

the investigation of this matter as well. 

7. Conclusion 

VASIR was evaluated using the ICE 2005 and 

MBGC/FOCS datasets. Using the ICE 2005 dataset, at 

FAR of 0.001, VASIR has a VR of 88.5% while IrisBEE is 

only 85.0% for the left eye. VASIR’s VR is 90.3% while 

IrisBEE’s VR is 85.2% for the right eye. For the EER 

criterion (where small is better), VASIR is ~4.4% for the 

left and right eyes while IrisBEE’s EER is considerably 

worse at twice the value. The results show that VASIR is 

superior to IrisBEE. 

The four HD shifting methods in VASIR were evaluated 

using the MBGC dataset: For classic-image to 

classic-image (CC), the matching HD scores are ordered by 

XorY, XandY, X, and then Y. For distant-image to 

classic-image (DC), XorY also leads with results superior 

to Y, X, followed by XandY. Interestingly, for 

distant-image to distant-image (DD), Y is better (opposite 

to the results of CC) and then XorY, X, XandY in that 

order. Overall, we choose XorY as best because XorY is 

best or second best in all three cases. 

We also provided a baseline of performance results on 

CC, DC, and DD ocular/iris recognition under a broad 

range of image and environmental conditions. 

The ocular recognition produced promising results. We 

showed that straightforward cosine correlation on the 

ocular regions yields a better ROC curve than the iris 

regions on the distant-images. When we combined XorY 

scores with the scores produced by cosine correlation the 

result was a better ROC curve than either of the two 

separately. These results show that making use of the ocular 

region aids in recognition compared to the iris region alone. 

Finally, our study shows that image quality is correlated 

with VASIR performance on DD matching case. Hence, 

the quality score metrics serve based on edge density as a 

predictor of VASIR performance. Various quality metrics 

on VASIR performance are currently in progress. 

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