Lauren R. Kennell

Iris image segmentation and sub-optimal images

Iris recognition is well developed and works well for optimal or near-optimal iris images. Dealing with sub-optimal images remains a challenge. Resolution, wavelength, occlusion and gaze are among the most important factors for sub-optimal images. In this paper, we explore the sensitivity of matching to these factors through analysis and numerical simulation, with particular emphasis on the segmentation portion of the processing chain.

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.

Using Artificial Neural Networks and Feature Saliency Techniques for Improved Iris Segmentation

One of the basic challenges to robust iris recognition is iris segmentation. This paper proposes the use of a feature saliency algorithm and an artificial neural network to perform iris segmentation. Many current Iris segmentation approaches assume a circular shape for the iris boundary if the iris is directly facing the camera. Occlusion by the eyelid can cause the visible boundary to have an irregular shape. In our approach an artificial neural network is used to statistically classify each pixel of an iris image with no assumption of circularity. First, a feed-forward feature saliency technique is performed to determine which combination of features contains the greatest discriminatory information. Image brightness, local moments, local orientated energy measurements and relative pixel location are evaluated for saliency. Next, the set of salient features is used as the input to a multi-layer perceptron feed-forward artificial neural network trained for classification. Testing showed 96.46 percent accuracy in determining which pixels in an image of the eye were iris pixels. For occluded images, the iris masks created by the neural network were consistently more accurate than the truth mask created using the circular iris boundary assumption. Post-processing to retain the largest contiguous piece in the iris mask increased the accuracy to 98.2 percent.

Iris recognition using the Ridge Energy Direction (RED) algorithm

The authors present a new algorithm for iris recognition. Segmentation is based on local statistics, and after segmentation, the image is subjected to contrast-limited, adaptive histogram equalization. Feature extraction is then conducted using two directional filters (vertically and horizontally oriented). The presence (or absence) of ridges and their dominant directions are determined, based on maximum directional filter response. Templates are compared using fractional Hamming distance as a metric for a match/non match determination. This ridge-energy-direction (RED) algorithm reduces the effects of illumination, since only direction is used. Results are presented that utilize four iris databases, and some comparison of recognition performance against a Daugman-based algorithm is provided.

Iris Recognition – Beyond One Meter

Iris recognition Iris recognition is, arguably, the most robust form of biometric Biometrics identification. It has been deployed in large-scale systems that have been very effective. The systems deployed to date make use of iris Remote Biometric cameras that require significant user cooperation; that in turn imposes significant constraints on the deployment scenarios that are practical.

Identifying discriminatory information content within the iris

The iris contains fibrous structures of various sizes and orientations which can be used for human identification. Drawing from a directional energy iris identification technique, this paper investigates the size, orientation, and location of the iris structures that hold stable discriminatory information. Template height, template width, filter size, and the number of filter orientations were investigated for their individual and combined impact on identification accuracy. Further, the iris was segmented into annuli and radial sectors to determine in which portions of the iris the best discriminatory information is found. Over 2 billion template comparisons were performed to produce this analysis.

Iris Segmentation for Recognition using Local Statistics

Iris recognition is one of the more commonly used biometrics for recognition due to its accuracy. One of the first steps in iris recognition is to segment the iris from the rest of the image for further processing. This paper investigates the use of local statistics to find the pupillary (inner) and limbic (outer) boundaries of the iris. In particular, the local standard deviation and local kurtosis have shown to be useful in this respect. The methodology and results using the University of Bath and the United States Naval Academy iris databases are presented.

Preprocessing of off-axis iris images for recognition

Iris recognition is an increasingly popular biometric due to its relative ease of use and high reliability. However, commercially available systems typically require on-axis images for recognition, meaning the subject is looking in the direction of the camera. The feasibility of using off-axis images is an important area of investigation for iris systems with more flexible user interfaces. The authors present an analysis of two image transform processes for off-axis images and an analysis of the utility of correcting for cornea refraction effects. The performance is assessed on the U.S. Naval Academy iris image database using the Ridge Energy Direction recognition algorithm developed by the authors, as well as with a commercial implementation of the Daugman algorithm.

An Artificial Neural Network Based Matching Metric for Iris Identification

The iris is currently believed to be the most accurate biometric for human identification. The majority of fielded iris identification systems are based on the highly accurate wavelet-based Daugman algorithm. Another promising recognition algorithm by Ives et al uses Directional Energy features to create the iris template. Both algorithms use Hamming distance to compare a new template to a stored database. Hamming distance is an extremely fast computation, but weights all regions of the iris equally. Work from multiple authors has shown that different regions of the iris contain varying levels of discriminatory information. This research evaluates four post-processing similarity metrics for accuracy impacts on the Directional Energy and wavelets based algorithms. Each metric builds on the Hamming distance method in an attempt to use the template information in a more salient manner. A similarity metric extracted from the output stage of a feed-forward multi-layer perceptron artificial neural network demonstrated the most promise. Accuracy tables and ROC curves of tests performed on the publicly available Chinese Academy of Sciences Institute of Automation database show that the neural network based distance achieves greater accuracy than Hamming distance at every operating point, while adding less than one percent computational overhead.

Iris matching with configurable hardware

Iris recognition systems have recently become an attractive identification method because of their extremely high accuracy. Most modern iris recognition systems are currently deployed on traditional sequential digital systems, such as a computer. However, modern advancements in configurable hardware, most notably Field-Programmable Gate Arrays (FPGAs) have provided an exciting opportunity to discover the parallel nature of modern image processing algorithms. In this study, iris matching, a repeatedly executed portion of a modern iris recognition algorithm is parallelized on an FPGA system. We demonstrate a 19 times speedup of the parallelized algorithm on the FPGA system when compared to a state-of-the-art CPU-based version.