A Robust Single-sensor Face and Iris Biometric Identification System based on Multimodal Feature Extraction Network

Joint face-iris identification can integrate complementary information from face and iris to fulfill the requirement of performance improvement and security. However, most of the current face-iris 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 non-ideal scenarios. In order to address these problems, a robust single-sensor 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 intra-class variations and enlarge inter-class variations. Our experimental results on CASIA.v4-distance and FRGC v2.0 non-ideal 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.