Jinyu Zuo

Image quality assessment for iris biometric

Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is the most reliable biometric in terms of recognition and identification performance. However, performance of these systems is affected by poor quality imaging. In this work, we extend previous research efforts on iris quality assessment by analyzing the effect of seven quality factors: defocus blur, motion blur, off-angle, occlusion, specular reflection, lighting, and pixel-counts on the performance of traditional iris recognition system. We have concluded that defocus blur, motion blur, and off-angle are the factors that affect recognition performance the most. We further designed a fully automated iris image quality evaluation block that operates in two steps. First each factor is estimated individually, then the second step involves fusing the estimated factors by using Dempster-Shafer theory approach to evidential reasoning. The designed block is tested on two datasets, CASIA 1.0 and a dataset collected at WVU. Considerable improvement in recognition performance is demonstrated when removing poor quality images evaluated by our quality metric. The upper bound on processing complexity required to evaluate quality of a single image is O(n2 log n), that of a 2D-Fast Fourier Transform.

Estimating and Fusing Quality Factors for Iris Biometric Images

Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is one of the most reliable biometrics in terms of recognition and identification performance. However, the performance of these systems is affected by poor-quality imaging. In this paper, we extend iris quality assessment research by analyzing the effect of various quality factors such as defocus blur, off-angle, occlusion/specular reflection, lighting, and iris resolution on the performance of a traditional iris recognition system. We further design a fully automated iris image quality evaluation block that estimates defocus blur, motion blur, off-angle, occlusion, lighting, specular reflection, and pixel counts. First, each factor is estimated individually, and then, the second step fuses the estimated factors by using a Dempster-Shafer theory approach to evidential reasoning. The designed block is evaluated on three data sets: Institute of Automation, Chinese Academy of Sciences (CASIA) 3.0 interval subset, West Virginia University (WVU) non-ideal iris, and Iris Challenge Evaluation (ICE) 1.0 dataset made available by National Institute for Standards and Technology (NIST). Considerable improvement in recognition performance is demonstrated when removing poor-quality images selected by our quality metric. The upper bound on computational complexity required to evaluate the quality of a single image is O(n2 log n).

A Robust IRIS Segmentation Procedure for Unconstrained Subject Presentation

Iris as a biometric, is the most reliable with respect to performance. However, this reliability is a function of the ideality of the data, therefore a robust segmentation algorithm is required to handle non-ideal data. In this paper, a segmentation methodology is proposed that utilizes shape, intensity, and location information that is intrinsic to the pupil/iris. The virtue of this methodology lies in its capability to reliably segment non-ideal imagery that is simultaneously affected with such factors as specular reflection, blur, lighting variation, and off-angle images. We demonstrate the robustness of our segmentation methodology by evaluating ideal and non-ideal datasets, namely CASIA, Iris Challenge Evaluation (ICE) data, WVU, and WVU Off-angle. Furthermore, we compare our performance to that of Camus and Wildes, and Libor Maseks algorithms. We demonstrate an increase in segmentation performance of 7.02%, 8.16%, 20.84%, 26.61%, over the former mentioned algorithms when evaluating these datasets, respectively.

On a Methodology for Robust Segmentation of Nonideal Iris Images

Iris biometric is one of the most reliable biometrics with respect to performance. However, this reliability is a function of the ideality of the data. One of the most important steps in processing nonideal data is reliable and precise segmentation of the iris pattern from remaining background. In this paper, a segmentation methodology that aims at compensating various nonidealities contained in iris images during segmentation is proposed. The virtue of this methodology lies in its capability to reliably segment nonideal imagery that is simultaneously affected with such factors as specular reflection, blur, lighting variation, occlusion, and off-angle images. We demonstrate the robustness of our segmentation methodology by evaluating ideal and nonideal data sets, namely, the Chinese Academy of Sciences iris data version 3 interval subdirectory, the iris challenge evaluation data, the West Virginia University (WVU) data, and the WVU off-angle data. Furthermore, we compare our performance to that of our implementation of Camus and Wildess algorithm and Maseks algorithm. We demonstrate considerable improvement in segmentation performance over the formerly mentioned algorithms.

On Generation and Analysis of Synthetic Iris Images

The popularity of iris biometric has grown considerably over the past two to three years. It has resulted in the development of a large number of new iris encoding and processing algorithms. Since there are no publicly available large-scale and even medium-size data bases, neither of the newly designed algorithms has undergone extensive testing. The designers claim exclusively high recognition performance when the algorithms are tested on a small amount of data. In a large-scale setting, systems are yet to be tested. Since the issues of security and privacy slow down the speed of collecting and publishing iris data, an optional solution to the problem of algorithm testing is to synthetically generate a large scale data base of iris images. In this work, we describe a model-based method to generate iris images and evaluate the performance of synthetic irises by using a traditional Gabor filter-based iris recognition system. A comprehensive comparison of synthetic and real data is performed at three levels of processing: 1) image level, 2) texture level, and 3) decision level. A sensitivity analysis is performed to conclude on the importance of various parameters involved in generating iris images

An Automatic Algorithm for Evaluating the Precision of Iris Segmentation

Recent developments in the field of nonideal iris recognition have shown that the presence of the degradations such as insufficient contrast, unbalanced illumination, out-of-focus, motion blur, specular reflections, and partial area affect performance of iris recognition systems. Most iris recognition systems are designed to implement a number of processing steps with iris segmentation being one of the first steps. If segmentation is not performed at a certain precision, the error of segmentation will further propagate and will be amplified during the proceeding processing, encoding, and matching steps. This emphasizes a critical need in designing robust iris segmentation algorithms and together with it a need of automatic algorithms evaluating the precision (accuracy) of iris segmentation. Automatic algorithm evaluating the precision of segmentation plays important role for two reasons: (1) it can be placed into a feedback loop to enforce another run of segmentation algorithm that may include more sophisticated steps for high precision segmentation and (2) the outcome of this evaluation can be treated as a quality factor and thus can be used to design a quality driven adaptive iris recognition system. This work analyzes effects of degradations on iris segmentation and proposes and tests an automatic algorithm evaluating the precision of iris segmentation.

Global and local quality measures for NIR iris video

In the field of iris-based recognition, evaluation of quality of images has a number of important applications. These include image acquisition, enhancement, and data fusion. Iris image quality metrics designed for these applications are used as figures of merit to quantify degradations or improvements in iris images due to various image processing operations. This paper elaborates on the factors and introduces new global and local factors that can be used to evaluate iris video and image quality. The main contributions of the paper are as follows. (1) A fast global quality evaluation procedure for selecting the best frames from a video or an image sequence is introduced. (2) A number of new local quality measures for the iris biometrics are introduced. The performance of the individual quality measures is carefully analyzed. Since performance of iris recognition systems is evaluated in terms of the distributions of matching scores and recognition probability of error, from a good iris image quality metric it is also expected that its performance is linked to the recognition performance of the biometric recognition system.

Cross spectral iris matching based on predictive image mapping

An adaptive method to predict NIR channel image from color iris images is introduced. Both visual inspection of the predicted image and the verification performance indicate that the adaptive mapping linking NIR image and color image is a potential solution to the problem of matching NIR images vs. color images in practice. When matched against NIR enrolled image the predicted NIR image achieves significantly high performance compared to the case when the same NIR image is matched against R channel alone.

Adaptive biometric authentication using nonlinear mappings on quality measures and verification scores

Three methods to improve the performance of biometric matchers based on vectors of quality measures associated with biometric samples are described. The first two methods select samples and matching scores based on predicted values of Quality of Sample (QS) index (defined here as d-prime) and Confidence in matching Scores (CS), respectively. The third method treats quality measures as weak but useful features for discrimination between genuine and imposter matching scores. The unifying theme for the three methods consists of a nonlinear mapping between quality measures and the predicted values of QS, CS, and combined quality measures and matching scores, respectively. The proposed methodology is generic and is suitable for any biometric modality. The experimental results reported show significant performance improvements for all the three methods when applied to iris biometrics.

Encoding, matching and score normalization for cross spectral face recognition: Matching SWIR versus visible data

We propose a methodology for cross matching color face images and Short Wave Infrared (SWIR) face images reliably and accurately. We first adopt a recently designed image encoding and matching technique which is capable to encode face images in both visible and SWIR spectral bands. Encoding is performed in two steps. Images are initially filtered with a bank of Gabor filters. Then three local operators: Simplified Weber Local Descriptor and Local Binary Pattern applied to magnitude of filtered images and Generalized Local Binary Pattern applied to the phase are involved to create histogram-like feature templates. The distance between two encoded face images is measured by symmetric I-divergence. The encoding and matching methods are demonstrated on long range SWIR data matched against close range visible images. A considerable performance improvement is observed compared to the results by FaceIt G8. To further enhance performance we propose an adaptive score normalization approach. We demonstrate that significant performance improvement is achieved with a small training set. Matching scores obtained by the proposed normalized method and by FaceIt G8 are fused to result in further performance improvement.