Cross-spectrum periocular authentication for NIR and visible images using bank of statistical filters

Periocular characteristics are being used as a supplementary feature for the biometric systems employing iris characteristics to mitigate effects of the noisy iris on authentication performance. In the same lines, ocular characteristics are also used to enhance the performance of face based systems under the impact of pose, expression and illumination. However, the iris and face systems are operated in Near-Infra-Red (NIR) and visible spectrum respectively. In order have both the systems work for ocular images and be compatible to each other, the biometric system needs to be robust enough to handle the biometric data emerging from different spectrum. In this work, we employ an ocular image database collected using the visible and NIR cameras. We propose a new framework employing a bank of Binarized Statistical Image filters along with χ2 distance metric along with simple fusion to handle the cross-spectrum data. Set of experiments conducted on the cross-spectrum periocular database indicate the robustness of the system with the achieved GMR of 96.04% at the FMR of 0.01%. The obtained performance indicates the applicability of proposed framework for realistic cross-spectrum biometric authentication scenario.