- Research Article
- Open Access
Iris Recognition: The Consequences of Image Compression
© RobertW. Ives et al. 2010
- Received: 11 November 2009
- Accepted: 9 March 2010
- Published: 26 April 2010
Iris recognition for human identification is one of the most accurate biometrics, and its employment is expanding globally. The use of portable iris systems, particularly in law enforcement applications, is growing. In many of these applications, the portable device may be required to transmit an iris image or template over a narrow-bandwidth communication channel. Typically, a full resolution image (e.g., VGA) is desired to ensure sufficient pixels across the iris to be confident of accurate recognition results. To minimize the time to transmit a large amount of data over a narrow-bandwidth communication channel, image compression can be used to reduce the file size of the iris image. In other applications, such as the Registered Traveler program, an entire iris image is stored on a smart card, but only 4 kB is allowed for the iris image. For this type of application, image compression is also the solution. This paper investigates the effects of image compression on recognition system performance using a commercial version of the Daugman iris2pi algorithm along with JPEG-2000 compression, and links these to image quality. Using the ICE 2005 iris database, we find that even in the face of significant compression, recognition performance is minimally affected.
- Compression Ratio
- Recognition Accuracy
- Image Compression
- Iris Image
- Probability Mass Function
Iris recognition is gaining popularity as the method of choice for human identification in society today. The iris, the colored portion of the eye that surrounds the pupil, contains unique patterns which are prominent under near-infrared illumination. These patterns are relatively permanent, remaining stable from a very young age, barring trauma or disease. They allow accurate identification with a very high level of confidence.
Commercial iris systems are used in a number of applications such as access to secure facilities or other resources, and even criminal/terrorist identification in the Global War on Terror. The identification process begins with enrollment of an individual into a commercial iris system, requiring the capture of one or more images from a video stream. Typically, the database for such a system does not contain actual iris images, but rather it stores a binary file that represents the distinctive information contained in each enrolled iris (called the template). Most commercial iris systems today use the Daugman algorithm [1–3]. In the Daugman algorithm, the template is stored as 512 bytes per eye.
Data compression is beginning to play a part in the employment of iris recognition systems. Law enforcement agencies, such as the Border Patrol, the Coast Guard, and even the Armed Forces, are using portable wireless iris recognition devices. In cases where the devices require a query to a master database for identification, it may be required to transmit captured images or templates over a narrow-bandwidth communication channel. In this case, minimizing the amount of data to transmit (which is possible through compression) minimizes the time to transmit, and saves precious battery power. There are other iris applications that require a full-resolution iris image to be carried on a smart card, but require a small fixed data storage size. An example is the Registered Traveler Interoperability Consortium (RTIC) standard, where only 4 kB is allocated on the RT smart card for the iris image . Since the standard iris image used for recognition is VGA-resolution (640 480, grayscale), it contains 307 kilobytes; significant compression would be required to fit a VGA iris image into 4 kilobytes. Applications of this nature serve as the primary motivation for this research.
This paper explores whether image compression can be utilized while maintaining recognition accuracy, and the effects on performance. We evaluate the effects of image compression on recognition using JPEG-2000 compression along with a commercial implementation of the Daugman recognition algorithm . The database used in this research is described in the following section.
Iris images used in this paper are available from the National Institute of Standards and Technology (NIST). The database of iris images used in this research is the Iris Challenge Evaluation (ICE) 2005 database . This iris database is composed of a total of 2953 iris images, collected from 132 subjects. Of these images, 1425 were of right eyes from 124 different individuals and 1528 were left eyes from 120 individuals. The images are all VGA resolution, 480 rows by 640 columns, with 8-bit grayscale resolution.
The JPEG-2000 algorithm is published by the Joint Photographic Experts Group (JPEG) as one of its still-image compression standards . JPEG-2000 uses state-of-the art compression techniques based on wavelets, unlike the more popular JPEG standard, which is based on the discrete cosine transform (DCT). JPEG-2000 contains options that allow both lossless and lossy compression of imagery, as does JPEG. When using any lossy compression technique, some information is lost in the compression and the amount and type of information that is lost depends on several factors, including the algorithm used for compression, the amount of compression desired (which determines the size of the compressed file), and special options offered in the algorithm such as Region-of-Interest (ROI) processing. In ROI processing, select regions of the image are deemed more important than other areas such that less information is lost in those regions.
The effect of image compression on iris recognition system performance has been addressed [8, 9]. In particular, in , iris images were compressed up to 50 : 1 using both JPEG-2000 and JPEG. In , Daugman and Downing used a portion of the ICE-2005 iris database and JPEG-2000 compression. Daugman used the Region-of-Interest (ROI) capability of JPEG-2000 which resulted in compression ratios of up to 145 : 1. He used segmentation methods to completely isolate the iris so as to reduce the size of the images from 480 640 to 320 320, and then completely discarded the regions of the smaller image that did not include the iris. Since the images were reduced in size to only contain the segmented iris, higher compression ratios were obtained with minimal effects on recognition performance. However, storing iris database images in this manner precludes testing of alternate segmentation methods. In our research, we opted to compress entire images rather than just the area of the iris-only information. This allows a more general approach to algorithm development research using a compressed iris database.
For this paper, we used the entire ICE-2005 database to obtain our results. We compressed the images using JPEG-2000, with the default parameters and options available in the JasPer implementation . The source code is freely available from the JasPer Project. We did not use the ROI capability, so that entire images were compressed as a whole and segmentation testing could be performed on compressed images.
Desired and actual compression ratios.
25 : 1
25.24 : 1
50 : 1
50.57 : 1
75 : 1
75.96 : 1
100 : 1
101.37 : 1
The information distance-based quality measure is used to evaluate the iris image quality [11, 12]. Prior to the application of the quality measure, the iris is first segmented and transformed to polar coordinates. This quality measure includes three parts: Feature Information Measure, Occlusion Measure, and Dilation Measure, which are then combined into a quality score. These three parts and the fusion to form the quality score are described below.
( 1) Feature Correlation Measure (FCM)
The compression process will introduce artificial iris patterns, which may have low correlation with the true patterns. Using this property, we applied the information distance (see ) between adjacent rows of the unwrapped image to measure the correlation within regions of the iris.
where is the Kullback-Leibler information distance, . In our algorithm, if there are values that do not appear within the selected portions of rows, they are not considered in the pmf to prevent a divide-by-zero condition in (1).
( 2) Occlusion Measure (O)
The total amount of invalid iris patterns can affect the recognition accuracy. Here, occlusion measure (O) is used to measure the percentage of the iris area that is invalid due to eyelids, eyelashes, and other noise.
( 3) Dilation Measure (D)
( 4) Score Fusion (Q)
In (4), = 0.005 and = 1/ . The value of was chosen experimentally. For most original images, the scores were above 0.005, while for compressed images most scores were lower than 0.005. The value is the normalization factor to ensure that when FCM = , f(FCM) = 1.
5.1. Performance Curves
Here, is the Hamming distance computed using (7), and n is the number of valid bits in the comparison. The value 911 is a scaling factor based on a typical number of bits used in comparisons. The normalization comes about to account for the number of valid bits actually used in computing the Hamming distance. In , the minimum number of bits used in the results is 400, and this is the minimum number of bits we allow in determining our results.
Number of matches in each database comparison.
Original versus Original
Original versus 25 : 1
Original versus 50 : 1
Original versus 75 : 1
Original versus 100 : 1
25 : 1 versus 25 : 1
25 : 1 versus 50 : 1
25 : 1 versus 75 : 1
25 : 1 versus 100 : 1
50 : 1 versus 50 : 1
50 : 1 versus 75 : 1
50 : 1 versus 100 : 1
75 : 1 versus 75 : 1
75 : 1 versus 100 : 1
100 : 1 versus 100 : 1
Minimum, mean And maximum HDs.
Original versus Original
Original versus 25 : 1
Original versus 50 : 1
Original versus 75 : 1
Original versus 100 : 1
Summary of performance results.
Best Accuracy (%)
FRR at FAR = 0.001
FRR at FAR = 0.0001
5.2. Quality Measure
As expected, and as shown in other researches, as iris images are compressed more, recognition performance reduces. The FAR remains fairly unaffected by changes in the image data, while the FRR is noticeably affected. The compression introduces artifacts into the iris images which alter the distinct patterns that are present in the original images, making the compressed images more dissimilar. There are some cases in which the compression introduced was small enough such that the templates of an original and the same image compressed by some amount resulted in the same template. The cases of zero Hamming distance between compression ratios came about due to a combination of small changes in the phase and mask bits so that none of the changed phase bits were actually counted. In general, though, the net effect is that comparing compressed images of the same eye will yield higher HDs, shifting the performance curve to the right and resulting in higher FRRs.
The importance of correct segmentation cannot be overemphasized. Poor segmentation will lead to poor results, and in fact can lead to false matches if too few bits are compared in computing the raw Hamming distance (7). The normalized Hamming distance (7) was developed to avoid this occurrence. Controls can be built into code to preclude this possibility if the number of bits compared between two templates is below some minimum number.
In general, when images are not compressed, images that have higher quality will generate higher recognition accuracy, as should be expected. When the images are compressed, the original image patterns within the iris will be suppressed and some new artificial compression artifacts/patterns will be added. This tends to decrease the recognition accuracy. As the compression rate increases, the recognition accuracy decreases. However, when using a small database, this effect may not be reflected in the recognition results. For some images in a small database, the compression process could introduce some stable unique patterns, which in some cases can increase the recognition accuracy. That is why we see the fluctuations in recognition accuracy across different compression rates, as well as fluctuations in the number of bits compared. In addition, different iris images would have different "reactions" to the compression due to the characteristics of the patterns. The quality of some images may be reduced dramatically due to the compression process, but some may not be.
Overall, the iris images in this research were subjected to considerable compression, and yet the recognition performance was only minimally affected. This is a significant, particularly when compared to the FBI's wavelet scalar quantization (WSQ) compression of fingerprint images. In the FBI standard, fingerprints can be WSQ compressed with loss to a maximum ratio of 15 : 1 , while in this research the images were compressed up to 100 : 1. This proves the effectiveness of JPEG-2000 compression, and its ability to preserve the important information in the compression process. Of further note, the iris images here were compressed without the benefit of the region-of-interest options available in JPEG-2000, which might allow even twice the compression with comparable results.
For the iCAP software implementation of the Daugman algorithm and advice on the use of the SDK, the authors gratefully acknowledge Dr. Jun Hong, Chief Scientist, Mr. Joseph Hwang, Senior Software Engineer, Mr. Samir Shah, Senior Software Engineer, and Mr. Tim Meyerhoff, Project Manager, LG Electronics U.S.A. Inc., Iris Technology Division. This work was supported in part by the Department of Defense and the National Institute of Justice (Award no. 2007-DE-BX-K182). This work was conducted under USNA IRB approval no. USNA.2007.0004-CR01-EM4-A.
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