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.

A Robust SingleSensor Face and Iris Biometric Identification System Based on Multimodal Feature Extraction Network Joint faceiris identification can integrate complementary information from face and iris to fulfill the requirement of performance improvement and security. However, most of the current faceiris multimodal biometric systems acquire face and iris with different sensors which brings about the increase of capturing complexity and device cost. Besides, they are limited by the identification performance degradation under nonideal scenarios. In order to address these problems, a robust singlesensor face and iris biometric identification system based on multimodal feature extraction (MFE) network is proposed. Only a single sensor is needed to obtain face and iris images in the proposed system, with the goal of improving recognition performance while minimizing sensor cost and acquisition time. The MFE network is designed as a general network module to extract both face and iris features and it is trained with a triplet framework to reduce intraclass variations and enlarge interclass variations. Our experimental results on CASIA.v4distance and FRGC v2.0 nonideal datasets show that the proposed system achieves better identification performance in terms of Equal Error Rate (EER) and False Reject Rate (FFR), etc. compared with other unimodal and multimodal biometric systems.