Robert W. Ives

Iris pattern extraction using bit planes and standard deviations

Iris recognition has been shown to be very accurate for human identification. In this paper, we develop a technique for iris pattern extraction utilizing the least significant bit-plane: the least significant bit of every pixel in the image. Through binary morphology applied to the bit-plane, the pupillary boundary of the iris is determined. The limbic boundary is identified by evaluating the standard deviation of the image intensity along the vertical and horizontal axes. Because our extraction approach restricts localization techniques to evaluating only bit-planes and standard deviations, iris pattern extraction is not dependent on circular edge detection. This allows for an expanded functionality of iris identification technology by no longer requiring a frontal view, which leads to the potential for off-angle iris recognition technology. Initial results show that it is possible to fit a close elliptical approximation to an iris pattern by using only bit-planes and standard deviations for iris localization.

Analysis of partial iris recognition using a 1D approach

Iris recognition has been shown to be very accurate for human identification. We investigate the performance of the use of a partial iris for recognition. A partial iris identification system based on a one-dimensional approach to iris identification is developed. Experiment results show that a more distinguishable and individually unique signal is found in the inner rings of the iris. The results also show that it is possible to use only a portion of the iris for human identification.

Use of one-dimensional iris signatures to rank iris pattern similarities

A one-dimensional approach to iris recognition is presented. It is translation-, rotation-, illumination-, and scale-invariant. Traditional iris recognition systems typically use a two-dimensional iris signature that requires circular rotation for pattern matching. The new approach uses the Du measure as a matching mechanism, and generates a set of the most probable matches (ranks) instead of only the best match. Since the method generates one-dimensional signatures that are rotation-invariant, the system could work with eyes that are tilted. Moreover, the system will work with less of the iris than commercial systems, and thus could enable partial-iris recognition. In addition, this system is more tolerant of noise. Finally, this method is simple to implement, and its computational complexity is relatively low.

A new approach to iris pattern recognition

An iris identification algorithm is proposed based on adaptive thresholding. The iris images are processed fully in the spatial domain using the distinct features (patterns) of the iris. A simple adaptive thresholding method is used to segment these patterns from the rest of an iris image. This method could possibly be utilized for partial iris recognition since it relaxes the requirement of using a majority of the iris to produce an iris template to compare with the database. In addition, the simple thresholding scheme can improve the computational efficiency of the algorithm. Preliminary results have shown that the method is very effective. However, further testing and improvements are envisioned.

Parallelizing Iris Recognition

Iris recognition is one of the most accurate biometric methods in use today. However, the iris recognition algorithms are currently implemented on general purpose sequential processing systems, such as generic central processing units (CPUs). In this work, we present a more direct and parallel processing alternative using field-programmable gate arrays (FPGAs), offering an opportunity to increase speed and potentially alter the form factor of the resulting system. Within the means of this project, the most time-consuming operations of a modern iris recognition algorithm are deconstructed and directly parallelized. In particular, portions of iris segmentation, template creation, and template matching are parallelized on an FPGA-based system, with a demonstrated speedup of 9.6, 324, and 19 times, respectively, when compared to a state-of-the-art CPU-based version. Furthermore, the parallel algorithm on our FPGA also greatly outperforms our calculated theoretical best Intel CPU design. Finally, on a state-of-the-art FPGA, we conclude that a full implementation of a very fast iris recognition algorithm is more than feasible, providing a potential small form-factor solution.

Iris recognition using histogram analysis

Iris recognition is perhaps the most accurate means of personnel identification due to the uniqueness of the patterns contained in each iris. Most commercial iris recognition systems use a patented algorithm based on two-dimensional Gabor wavelets developed by Daugman. This paper describes an alternate means to identify individuals using images of their iris. Here, we simplify the process by using preprocessed one-dimensional histograms. The methodology in forming these histograms, how they are used in enrollment and identification and performance in terms of false positives and false negatives are presented.

Compression of complex-valued SAR images

Synthetic aperture radars (SAR) are coherent imaging systems that produce complex-valued images of the ground. Because modern systems can generate large amounts of data, there is substantial interest in applying image compression techniques to these products. We examine the properties of complex-valued SAR images relevant to the task of data compression. We advocate the use of transform-based compression methods but employ radically different quantization strategies than those commonly used for incoherent optical images. The theory, methodology, and examples are presented.

Effects of image compression on iris recognition system performance

The human iris is perhaps the most accurate biometric for use in identification. Commercial iris recognition systems currently can be found in several types of settings where a person’s true identity is required: to allow passengers in some airports to be rapidly processed through security; for access to secure areas; and for secure access to computer networks. The growing employment of iris recognition systems and the associated research to develop new algorithms will require large databases of iris images. If the required storage space is not adequate for these databases, image compression is an alternative. Compression allows a reduction in the storage space needed to store these iris images. This may, however, come at a cost: some amount of information may be lost in the process. We investigate the effects of image compression on the performance of an iris recognition system. Compression is performed using JPEG-2000 and JPEG, and the iris recognition algorithm used is an implementation of the Daugman algorithm. The imagery used includes both the CASIA iris database as well as the iris database collected by the University of Bath. Results demonstrate that compression up to 50:1 can be used with minimal effects on recognition.

A multidisciplinary approach to biometrics

Biometrics is an emerging field of technology using unique and measurable physical, biological, or behavioral characteristics that can be processed to identify a person. It is a multidisciplinary subject that integrates engineering, statistics, mathematics, computing, psychology, and policy. The need for biometrics can be found in governments, in the military, and in commercial applications. The Electrical Engineering Department at the U.S. Naval Academy, Annapolis, MD, has introduced a biometric signal processing course for senior-level undergraduate students and has developed a biometrics lab to support this course. In this paper, the authors present the course content, the newly developed biometric signal processing lab, and the interactive learning process of the biometric course. They discuss some of the challenges that were encountered in implementing the course and how they were overcome. They also provide some feedback from the course assessment.

Iris recognition: the consequences of image compression

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.