Robust Iris Recognition Baseline 

 for the Grand Challenge

Yooyoung Lee

Ross J. Micheals

James J. Filliben

P. Jonathon Phillips

NISTIR 7777
NISTIR 7777

Robust Iris Recognition Baseline 

for the Grand Challenge

Yooyoung Lee

Ross J. Micheals

P. Jonathon Phillips

Information Access Division 

Information Technology Laboratory 

James J. Filliben

Statistical Engineering Division 

Information Technology Laboratory 

National Institute of Standards and Technology 

Gaithersburg, MD 20899 

March 2011

 U.S. Department of Commerce 

Gary Locke, Secretary 

National Institute of Standards and Technology 

Patrick D. Gallagher, Director
Abstract 

Due to its distinctiveness, the human iris is a popular biometric feature used to identity a 

person with high accuracy. The “Grand Challenge” in iris recognition is to have an effective 

algorithm for subject verification or identification under a broad range of image and 

environmental conditions. This paper presents both baseline performance results and an 

enhanced version of VASIR (Video-based Automated System for Iris Recognition) as a 

response the challenge. We describe the details of the VASIR procedure and demonstrate its 

superiority over the IrisBEE algorithm. Finally, the image quality scores and how they relate 

to VASIR’s performance are examined. 

Keywords − biometrics, iris recognition, VASIR, baseline, benchmark, Hamming distance, 

 image processing 
1. Introduction 

Human physiological or behavioral characteristics can be integrated into a system to identify a per

son; such a system is called a Biometric system. Specifically, the human iris is a highly distinctive fea

ture that can provide a promising security application which we call iris recognition. 

Current iris recognition, however, is often limited by significant input and environmental 

restrictions of the image acquisition. For example, an iris recognition system might perform well only 

for high quality images, which were captured with devices specially designed for the system. In the 

iris recognition community, the “Grand Challenge” is to create a robustly effective algorithm for 

subject identification that yields accurate 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) system which we call “distant-video-based” contrasted with a classical still-image acquisition 

system such as the LG 2200 EOU which we call “classical-still-based” iris recognition. 

Video-based iris recognition at a distance 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 video-based iris recognition, to motivate other researchers to promote their iris technology and to 

address current problems. 

This paper also describes a particular iris recognition system VASIR (Video-based Automated 

System for Iris Recognition) which was developed to address such limitations that were shown in our 

existing works. A distinguishing part of VASIR is its ability to automatically detect the eye region 

and then to automatically select the best quality iris image from NIR face visible video captured at a 

distance. After such acquisition processing, VASIR carries out image comparison analysis to yield an 

individual verification [1]. 

To establish additional robustness in the VASIR system, two significant updates have been 

implemented: 

1) Morphological preprocessing for improved accuracy of iris region segmentation, and 

2) Modified Hamming distance (HD) matching methodology for improved false reject rates (FRR)

We also describe which performance metrics (e.g., false accept rate) are applied for the quantitativ

e comparison of an algorithm’s performance. In addition, this paper provides a detailed explanation of

 the test protocol that was utilized for our experiments. 

To assess VASIR’s performance, we address the following three specific questions: 

1) Is VASIR robustly superior to IrisBEE? 

2) Can VASIR’s results be used as a state-of-the-art baseline for both classical-still-based and 

distant-video-based iris recognition? 

3) How does VASIR performance change with the image quality?

The system was evaluated using ICE (Iris Challenge Evaluation) 2005 and MBGC (Multiple Biom

etrics Grand Challenge) image datasets (publicly available) provided by the National Institute of Stan

dards and Technology (NIST) [2][3]. 

Section 2 gives a brief overview of IrisBEE and VASIR. Section 2 describes the ICE and MBGC d

atasets. Section 3 discusses a new approach of segmentation and matching methods. Section 5 describ

es in detail VASIR’s performance results vis-à-vis the three above-mentioned questions. 
2. Overview of IrisBEE and VASIR 

Most iris-based systems currently in existence are for controlled classical-still-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 classical-still-based iris recognition. 

IrisBEE – as shown in Figure 1 – is comprised of five distinct steps for iris recognition. In the first 

step, 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. The segmentation in 

the second step isolates the iris region from the rest of the acquired image. The isolated iris region is 

then normalized for comparison to another. Next, the features of the iris pattern are extracted from the 

normalized image and encoded to generate the iris template. Finally, the created iris template is 

compared with a template generated from an enrolled iris image. 

Figure 1 IrisBEE procedure

In contrast to the IrisBEE’s algorithm, VASIR was developed with less constrained distant-videobased iris recognition in mind. VASIR also robustly allows performing classical-still to classical-still 

(SvsS), distant-video to classical-still (VvsS), and distant-video to distant-video (VvsV) iris 

recognition. In SvsS matching, a classical still-image is matched against other classical still-images of 

the same person that were captured by the same device (LG2200 EOU system). VvsS means that the 

video-frame captured at a distance (IOM system) is compared to classical still-images, captured by a 

different camera. In VvsV matching, the extracted iris region of distant-video frames is matched to 

other frames either from the same video or different video sequences of the same person. 

Figure 2 VASIR procedure (a case of video-distant vs classical-still)
Figure 2 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). 

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. Datasets 

A. 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 Num. of Images Num. 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, and consequently was set aside in our 

study [2]. 

B. MBGC (Multiple Biometrics Grand Challenge) 

Our VASIR performance study used two different data types included in the MBGC dataset. 

The near-infrared facial video (NIR_Face_Video or distant-video) were captured by an Iris On the 

Move (IOM) system; each frame is 2048x2048 pixels and the iris diameter is ~100 pixels. The nearinfrared iris still images (NIR_Iris_Still or classical-still) were collected by an LG2200 EOU and are 

640x480 in resolution and the iris diameter is ~200 pixels [12]. 

Table 2 NIR_Face_Video captured by IOM system 

NIR_Face_Video Version 1 Version 2 

Num. of the total video 149 628 

Num. of both eyes visible 130 598 

Total frames 2,534 11,341 
Table 3 NIR_Iris_Still captured by LG2200 EOU system 

NIR_Iris_Still Version 1 Version 2 

Left 834 4,025 

Right 834 4,013 

Figure 3 Illustration of differences between (a)−(b) NIR_Face_Video captured 

by a IOM and (c) − (d) NIR_Iris_Still captured by a LG2200 EOU; 

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

The detailed numbers for distant-video and classical-still iris images can be found in Table 2 and 

3. MBGC has two versions of datasets. MBGCv2 is the larger dataset that are included the MBGCv1. 

Figure 3 shows the differences between NIR_Face_Video and NIR_Iris_Still in the MBGC dataset. 
4. Segmentation and Matching 

A. Segmentation 

In earlier version of VASIR, the segmentation components first detected the pupil boundary by 

fitting an ellipse to the contour points. In order to reduce noise in an image, a Gaussian filter was 

applied to the image before getting the binary threshold image [7]. Even though this led to better pupil 

segmentation results compared to IrisBEE, this was still not an optimal solution for reducing noise in 

images caused by blurriness, eyelashes, eyelid, glasses, lens distortion, reflections, or shadows. 

Figure 4 Example of noise reduction 

VASIR now applies a Gaussian filter to the binary threshold image and uses morphological 

opening and closing later on. Opening of an image is an erosion followed by a dilation to remove 

small objects from a binary image while preserving the shape and size of larger objects within the 

image. Closing on the other hand consists of dilation followed by an erosion to eliminate small holes 

and fill gaps in the image [8]. However, the process of finding optimal threshold value is possibly 

slower than the previous method since the threshold is calculated differently depending on the amount 

of noise within an image. 

For the 254 (left and right) eye images that were selected by AIQM from distant video in 

MBGCv1, the previous segmentation rate of pupil boundary in VASIR was 91.9%. Using this new 

method, the segmentation rate improved to 93.3%. As shown in Figure 4, we found this method to be 

much more effective for reducing the noise. 

The iris boundary is segmented by using a circular Hough transform initially applied by Wildes et 

al [9]. We use the information of the detected pupil circle as a reference to detect the iris boundary. 

The eyelid curves are segmented by detecting four points and fitting two ellipses; we will refer to 

them as upper and lower eyelid points and left and right eyelid corners. The upper and lower eyelid 

points are detected by a linear Hough transform. For the left and right eyelid corners, our algorithm 

retrieves all contours that link horizontal segments. 

B. Matching 

The Hamming distance (HD) counts the number of positions (in two templates of the same size) 

for which the binary digits are different [10]. 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 uniquelydefined due to its gradual gradient change from the iris to the sclera. Figure 5 illustrates a case of lessthan-optimal segmentation of the pupil (a) and (b) iris boundaries. 

Figure 5 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 6 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 one bit 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 6 VASIR matching method 

For all three matching cases –details are described in section V – of a) classical-still to classicalstill (SvsS) b) distant-video to classical-still (VvsS), and c) distant-video to distantvideo (VvsV) in Figure 7 illustrates the Hamming distance (HD) comparison of the X, Y, XorY, and 

XandY matching methods for both the left and right eye based on the MBGCv2 dataset. For SvsS, the

 matching scores are ordered by XorY, XandY, X, and then Y methods consequence to the results fro

m the ICE2005 dataset. XorY also leads with the best results followed by the Y, X, and XandY for Vv

sS. Interestingly, Y is better for VvsV, (opposite to the results of SvsS) and then XorY, X, XandY foll

ow in order. Overall, XorY is better because XorY is best or second best in all three cases. 
5. Experiments 

In this section, we describe which performance metrics are applied and how three different 

experiments are designed and executed to evaluate VASIR performance. 

Biometric systems are conventionally evaluated by the True Accept Rate (TAR) 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 Error Equal 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 Detection Error Trade-off (DET) curve is a graph of 

FAR against FRR. The Receiver Operator Characteristic (ROC) curve is a plot of TAR against FAR. 

The DET and ROC curves are useful, but, neither of these plots provides the detailed threshold (e.g., 

HD) information needed to estimate the system performance. We therefore add two additional curves 

to evaluate VASIR performance: False Curves with Threshold distribution (FCT), and True Curves 

with Threshold distribution (TCT). FCT is the plot of FAR and FRR against  ; whereas TCT is the 

opposite; TAR and TRR against  . These curves allow us to analyze FAR and FRR with EER and to 

represent the discrepancy between matching and non-matching distribution at the same time. 

In terms of Verification (1:1), an image matches if its matching score has satisfied a certain 

threshold , whereas Identification (1:Many) indicates that an image matches if its score is the best. 

Our results are based on verification. 

A. 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 nonmatching. As mentioned in section four, only classical-still to classical-still matching is allowed for 

this dataset. It is of note that the images for the subjects were captured on different days. 

Figure 7 Comparison of four matching methods for each of three data types 

(a) classical-still to classical-still (SvsS) (b) distant-video to classical-still (VvsS) 

(c) distant-video to distant-video (VvsV) 
The four types of matching methods (HD_X, HD_Y, HD_XorY and HD_XandY) - mentioned in 

section 3 – were evaluated with two criteria for the comparison; 1) TAR at FAR of 0.001 (for which 

higher is better) and 2) the EER value (for which smaller is deemed as better). 

The performance results from VASIR’s HD_XorY has the highest TAR at FAR of 0.001 and the 

smallest EER both for the left and right dataset in ICE2005; HD_XandY, HD_X and HD_Y follow in 

order. Figure 8 shows the comparison between IrisBee's and VASIR's results by plotting the ROC 

curve. The plot visualizes IrisBEE (HD_X) and VASIR (HD_XorY). 

For the left eye, at FAR of 0.001, VASIR has a TAR of 88.5% while IrisBEE is only 85.0%. For 

the right eye, VASIR’s TAR is 90.3% while IrisBEE’s TAR 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. 

Figure 8 Comparison of VASIR and IrisBEE for the left and right eye 

(classical-still to classical-still iris images matching) 

 

The results show that even though VASIR was developed primarily as a tool for distant-videobased iris recognition, the system is robust for classical-still-based iris recognition as well. 

B. Baseline for Benchmarking 

Can VASIR’s results be used as a state-of-the-art baseline for both classical-still-based and 

distant-video-based iris recognition? We present the performance results based on the MBGCv2 

dataset using VASIR for the purpose of providing a baseline both under unconstrained distant-videobased and constrained classical-still-based 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 iris recognition algorithms and to promote their 

technology. 

For distant-video data, the eye region was detected in 598 videos out of the total 628 videos. In 30 

videos that were included either no eyes were visible in the video or the file was erroneous. The 

number of extracted iris images for the left and right eye was 9,592 out of the total 11,341 frames. No 
eye region could be detected in 41 images - out of the total 9,592 images (false detection rate: 0.43%). 

For classical-still 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 performan

ce results. 

(a) (b) (c)

Figure 9 Three matching protocol 

(a) classical-still to classical-still (SvsS) (b) distant-video to classical-still (VvsS) 

(c) distant-video to distant-video (VvsV) 

First, VASIR allows us to match 1) classical-still to classical-still (SvsS) 2) distantvideo to classical-still (VvsS), and 3) distant-video to distant-video (VvsV). For SvsS and VvsV (allframe to all−frame) matching scores, the query image qi matches the target images ti where i is the sa

me person, but a different image regardless of the captured date of video sequences or still images. In 

VvsS matching scores, the query matches all targets where the subject is the same person. An advanta

ge of this protocol is that the user does not have to repeatedly generate the mask for each dataset, the s

ystem is using the subject ID which is part of the file name. Figure 9 shows how matching scores were

 calculated for each procedure. 

Figure 10 Comparison of full non-matching (1,638,949 entries) and 

random non-matching (201,250 entries) out of 4,025 NIR_Iris_Still images; 

There are at least 6 images per person per eye
For non-matching scores, the query qi compares against 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 nonmatching 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. Figure 10 provides 

the DET curve obtained from all left eye images on MBGCv2 SvsS non-matching. The results show 

that in comparison full non-matching and random non-matching are nearly equivalent. 

In Figure 11 (four plots of the left and right ROC and FCT curves), we compare the performance r

esults of SvsS, VvsS, and VvsV matching using HD_XorY matching method based on the results as s

hown in Figure 7. The detailed error rates are described in Table 4. The results show that SvsS matchi

ng has the best performance; VvsS and VvsV follow next as expected. 

Table 4 VASIR’s Performance results 

Datasets 

TAR at FAR = 0.01 

EER 

LEFT RIGHT 

SvsS 89.5% 89.8% 6.7% 

VvsS 25.3% 33.5% 30.9% 

VvsV 24.5% 33.1% 32.6% 

Figure 11 Comparison of SvsS, VvsS, and VvsV for the left and right
 

C. 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 t

he pixel grid. The Dx and Dy are applied separately to the input image to measure the gradient compo

nent in each orientation. The gradient magnitude, which a higher score is considered better image qual

ity in our evaluation, can be given by: 

2 2 Q  Dx  Dy

The quality score from Flynn et al [15] is measured for a pair of samples q and t and is defined as, 

Q(q,t)  min(Q(q),Q(t))

To calculate a quality measure Q(q,t) for a set of match scores M and a set of nonmatch scores N, decision threshold and image quality threshold  are given as parameters. Therefor

e, TAR( , ) and FAR( , ) are computed as defined, 

 

 

   

 

     # ( , )# ( , ) ( , ) ( , ) M Q q t

M S q t Q q t TAR

M is the set of match similarity scores and S(q,t) is the similarity score where the two images q and

 t are of the same person. The TAR is the fraction of similarity score S(q,t) greater than or equal to a de

cision threshold , where the quality score Q(q,t) is greater than or equal to a threshold . 

 

 

   

 

     # ( , )

# ( , ) ( , ) ( , ) N Q q t

N S q t Q q t FAR

N is the set of non-match similarity scores and S(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 S(q,t) greater than or 

equal to a decision threshold  , where the quality score Q(q,t) is greater than or equal to a 

threshold . 
 

Figure 12 Distribution of quality score (edge desnity) and Hamming distance (XorY) 

on VvsV matching and non-matching 

Figure 12 shows the distribution of the quality scores and Hamming distances (XorY) based on 

left and right images for distant-video to distant-video (VvsV). The plot illiustrates that the VvsV 

dataset consists of poor quality images; the scatter points are primarily found on the left side. 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 VvsV matching, while the non-matching has only -0.11. Figure 13 depicts 

actual examples of VvsV highest and lowest quality score. 

Figure 13 Samples ordered by quality score (edge density) 

(a) VvsV: Highest score image (341x200px) (b) VvsV: Lowest score image (506x301px) 
 

Figure 14 VASIR’s performance on the image quality for distant-video to distant-video matching 

Following, the False Reject Rate (FRR) is calculated by applying six levels of the image quality. 

In case of the left eye images, the quality score for VvsV is calculated with the edge density ranges 

approximately from 10 to 35. We define a set of six quality levels (0 to 5) with thresholds { 0  ... 5  }; 

Quality level 0 (Q0) is all images; Quality level 1 (Q1) means all images with quality score 1  ; the 

remaining levels are Q2 2  , Q3 3  , Q4 4  , and Q5 5  (best). 

For each quality level, VASIR’s performance is evaluated with an FRR value of FAR at 0.01, 

using only samples of all quality levels above the current level. Figure 14 illustrates the VvsV 

performance results of the eye images with the matching method by applying HD_XorY. 

The plots in Figure 14 show that the FRR in VvsV performance 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. The possible problem is that 

the quality score will be high if the image includes a lot of noise such as 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 VvsV matching. However, there is a need for 

analyzing quality metrics for good image quality datasets such as classical-still iris images. We are 

pursuing the investigation of this matter as well. 
 

6. Conclusion 

VASIR was evaluated using the ICE 2005 and MBGC datasets. Using the ICE 2005 dataset, at 

FAR of 0.001, VASIR has a TAR of 88.5% while IrisBEE is only 85.0% for the left eye. VASIR’s 

TAR is 90.3% while IrisBEE’s TAR 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 matching methods in VASIR were evaluated using the MBGC dataset: For classical-still 

to classical-still (SvsS), the matching HD scores are ordered by XorY, XandY, X, and then Y. For 

distant-video to classical-still (VvsS), XorY also leads with results superior to Y, X, followed by 

XandY. Interestingly, for distant-video to distant-video (VvsV), Y is better (opposite to the results of 

SvsS) and then XorY, X, XandY in that order. Overall, XorY is better because XorY is best or second 

best in all three cases. 

We also provided a baseline of performance results on SvsS, VvsS, and VvsV iris recognition 

under a broad range of image and environmental conditions. 

Finally, our study shows that image quality is correlated with VASIR performance on VvsV 

matching. Hence, the quality score metrics may be served as a predictor of VASIR performance. 

Various quality metrics on VASIR performance are currently in progress. Upon approval of manag

ement, we plan to release the VASIR algorithm. 

ACKNOWLEDGMENT

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

endorsement or recommendation by the National Institute of Standards and 

Technology. 

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