A fusion approach to unconstrained iris recognition

As biometrics has evolved, the iris has remained a preferred trait because its uniqueness, lifetime stability and regular shape contribute to good segmentation and recognition performance. However, commercially deployed systems are characterized by strong acquisition constraints based on active subject cooperation, which is not always achievable or even reasonable for extensive deployment in everyday scenarios. Research on new techniques has been focused on lowering these constraints without significantly impacting performance while increasing system usability, and new approaches have rapidly emerged. Here we propose a novel fusion of different recognition approaches and describe how it can contribute to more reliable noncooperative iris recognition by compensating for degraded images captured in less constrained acquisition setups and protocols under visible wavelengths and varying lighting conditions. The proposed method was tested at the NICE.II (Noisy Iris Challenge Evaluation – Part 2) contest, and its performance was corroborated by a third-place finish.

► Analysis of different biometric recognition techniques.
► Fusion of such methods to achieve recognition on noisy iris data.
► Individual and global performance, with analysis of the improvement.
► Evaluation on both verification and identification modes.
► Conclusions show the applicability of our study on non-cooperative scenarios.