Obtaining Stable Iris Codes Exploiting LowRank Tensor Space and Spatial Structure Aware Refinement for Better Iris Recognition The strength of iris recognition in terms of optimal biometric performance has been challenged by inevitable operational conditions in unconstrained scenarios. In this work we present a new approach for extracting stable iris weight maps to account for the noisy iris representation as a result of capture conditions and ineluctable segmentation errors. Traditional approaches to extract stable bits often ignore intercode relations under the presence of multiple enrolment samples. Unlike previous works, we formulate the stable code extraction using tensor representation to exactly recover the lowrank nonnoisy iris information using the multiple enrolment samples. Further, the proposed approach produces stable class specific (user specific) iris weight maps by eliminating the error bits due to suboptimal segmentation or pupil dilation effects using spatial correspondence in a patchwise manner. Through the set of experiments on two publicly available iris databases acquired under semiconstrained and unconstrained setting, we demonstrate the superiority for identification and verification performance over current stateoftheart algorithms. Rankxe2x88x921 identification rate on CASIAv4 distance database is achieved at 93.3% and a verification accuracy of Genuine Match Rate (GMR) of 80% at False Match Rate(FMR) of 0.0001 indicating the applicability of proposed approach in operational scenarios.

Iris Feature Extraction and Matching Method for Mobile Biometric Applications Biometric methods are increasingly penetrating the field of mobile applications, confronting researchers with a huge number of problems that have not been considered before. Many different interaction scenarios in conjunction with the mobile device performance limitations challenge the capabilities of onboard biometrics. Saturated with complex textural features the iris image is used as a source for the extraction of unique features of the individual that are used for recognition. The mentioned features inherent to the interaction with the mobile device affect not only the source image quality but natural deformations of the iris leading to high intraclass variations and hence reducing the recognition performance. A novel method for iris feature extraction and matching is represented in this work. It is based on a lightweight CNN model combining the advantages of a classic approach and advanced deep learning techniques. The model utilizes shallow and deep feature representations in combination with characteristics describing the environment that helps to reduce intraclass variations and as a consequence the recognition errors. It showed high efficiency on the mobile and a few more datasets outperforming stateoftheart methods by far.