Advancing iris biometric technology

Abstract

The iris biometric is a well-established technology which is already in use in several nation-scale applications and it is still an active research area with several unsolved problems. This work focuses on three key problems in iris biometrics namely: segmentation, protection and cross-matching. Three novel methods in each of these areas are proposed and analyzed thoroughly. In terms of iris segmentation, a novel iris segmentation method is designed based on a fusion of an expanding and a shrinking active contour by integrating a new pressure force within the Gradient Vector Flow (GVF) active contour model. In addition, a new method for closed eye detection is proposed. The experimental results on the CASIA V4, MMU2, UBIRIS V1 and UBIRIS V2 databases show that the proposed method achieves state-of-theart results in terms of segmentation accuracy and recognition performance while being computationally more efficient. In this context, improvements by 60.5%, 42% and 48.7% are achieved in segmentation accuracy for the CASIA V4, MMU2 and UBIRIS V1 databases, respectively. For the UBIRIS V2 database, a superior time reduction is reported (85.7%) while maintaining a similar accuracy. Similarly, considerable time improvements by 63.8%, 56.6% and 29.3% are achieved for the CASIA V4, MMU2 and UBIRIS V1 databases, respectively. With respect to iris biometric protection, a novel security architecture is designed to protect the integrity of iris images and templates using watermarking and Visual Cryptography (VC). Firstly, for protecting the iris image, text which carries personal information is embedded in the middle band frequency region of the iris image using a novel watermarking algorithm that randomly interchanges multiple middle band pairs of the Discrete Cosine Transform (DCT). Secondly, for iris template protection, VC is utilized to protect the iii iris template. In addition, the integrity of the stored template in the biometric smart card is guaranteed by using the hash signatures. The proposed method has a minimal effect on the iris recognition performance of only 3.6% and 4.9% for the CASIA V4 and UBIRIS V1 databases, respectively. In addition, the VC scheme is designed to be readily applied to protect any biometric binary template without any degradation to the recognition performance with a complexity of only O(N). As for cross-spectral matching, a framework is designed which is capable of matching iris images in different lighting conditions. The first method is designed to work with registered iris images where the key idea is to synthesize the corresponding Near Infra-Red (NIR) images from the Visible Light (VL) images using an Artificial Neural Network (ANN) while the second method is capable of working with unregistered iris images based on integrating the Gabor filter with different photometric normalization models and descriptors along with decision level fusion to achieve the cross-spectral matching. A significant improvement by 79.3% in cross-spectral matching performance is attained for the UTIRIS database. As for the PolyU database, the proposed verification method achieved an improvement by 83.9% in terms of NIR vs Red channel matching which confirms the efficiency of the proposed method. In summary, the most important open issues in exploiting the iris biometric are presented and novel methods to address these problems are proposed. Hence, this work will help to establish a more robust iris recognition system due to the development of an accurate segmentation method working for iris images taken under both the VL and NIR. In addition, the proposed protection scheme paves the way for a secure iris images and templates storage. Moreover, the proposed framework for cross-spectral matching will help to employ the iris biometric in several security applications such as surveillance at-a-distance and automated watch-list identification.