Accurate iris recognition from the distantly acquired face or eye images under less constrained environments require development of specialized strategies which can accommodate for significant image variations (e.g., scale, rotation, translation) and influence from multiple noise sources. A set of coordinate-pairs, which is referred to as geometric key in this paper is randomly generated and exclusively assigned to each subject enrolled into the system. Such geometric key uniquely defines the way how the iris features are encoded from the localized iris region pixels. Such iris encoding scheme involves computationally efficient and fast comparison operation on the locally assembled image patches using the locations defined by the geometric key. The image patches involved in such operation can be more tolerant to the noise. Scale and rotation changes in the localized iris region can be well accommodated by using the transformed geometric key. The binarized encoding of such local iris features still allows efficient computation of their similarity using Hamming distance. The superiority of the proposed iris encoding and matching strategy is ascertained by providing comparison with several state-of-the-art iris encoding and matching algorithms on three publicly available databases: UBIRIS.v2, FRGC, CASIA.v4-distance, which suggests the average improvements of 36.3%, 32.7%, and 29.6% in equal error rates, respectively, as compared with several competing approaches.

Accurate iris recognition from the distantly acquired face or eye images requires development of effective strategies which can account for significant variations in the segmented iris image quality. Such variations can be highly correlated with the consistency of encoded iris features and the knowledge that such fragile bits can be exploited to improve matching accuracy. A non-linear approach to simultaneously account for both local consistency of iris bit and also the overall quality of the weight map is proposed. Our approach therefore more effectively penalizes the fragile bits while simultaneously rewarding more consistent bits. In order to achieve more stable characterization of local iris features, a Zernike moment-based phase encoding of iris features is proposed. Such Zernike moments-based phase features are computed from the partially overlapping regions to more effectively accommodate local pixel region variations in the normalized iris images. A joint strategy is adopted to simultaneously extract and combine both the global and localized iris features. The superiority of the proposed iris matching strategy is ascertained by providing comparison with several state-of-the-art iris matching algorithms on three publicly available databases: UBIRIS.v2, FRGC, CASIA.v4-distance. Our experimental results suggest that proposed strategy can achieve significant improvement in iris matching accuracy over those competing approaches in the literature, i.e., average improvement of 54.3%, 32.7% and 42.6% in equal error rates, respectively for UBIRIS.v2, FRGC, CASIA.v4-distance.

Online iris recognition using distantly acquired images in a less imaging constrained environment requires the development of a efficient iris segmentation approach and recognition strategy that can exploit multiple features available for the potential identification. This paper presents an effective solution toward addressing such a problem. The developed iris segmentation approach exploits a random walker algorithm to efficiently estimate coarsely segmented iris images. These coarsely segmented iris images are postprocessed using a sequence of operations that can effectively improve the segmentation accuracy. The robustness of the proposed iris segmentation approach is ascertained by providing comparison with other state-of-the-art algorithms using publicly available UBIRIS.v2, FRGC, and CASIA.v4-distance databases. Our experimental results achieve improvement of 9.5%, 4.3%, and 25.7% in the average segmentation accuracy, respectively, for the UBIRIS.v2, FRGC, and CASIA.v4-distance databases, as compared with most competing approaches. We also exploit the simultaneously extracted periocular features to achieve significant performance improvement. The joint segmentation and combination strategy suggest promising results and achieve average improvement of 132.3%, 7.45%, and 17.5% in the recognition performance, respectively, from the UBIRIS.v2, FRGC, and CASIA.v4-distance databases, as compared with the related competing approaches.

Remote human identification using iris biometrics has high civilian and surveillance applications and its success requires the development of robust segmentation algorithm to automatically extract the iris region. This paper presents a new iris segmentation framework which can robustly segment the iris images acquired using near infrared or visible illumination. The proposed approach exploits multiple higher order local pixel dependencies to robustly classify the eye region pixels into iris or noniris regions. Face and eye detection modules have been incorporated in the unified framework to automatically provide the localized eye region from facial image for iris segmentation. We develop robust postprocessing operations algorithm to effectively mitigate the noisy pixels caused by the misclassification. Experimental results presented in this paper suggest significant improvement in the average segmentation errors over the previously proposed approaches, i.e., 47.5%, 34.1%, and 32.6% on UBIRIS.v2, FRGC, and CASIA.v4 at-a-distance databases, respectively. The usefulness of the proposed approach is also ascertained from recognition experiments on three different publicly available databases.