Cross-Spectral Periocular Recognition by Cascaded Spectral Image Transformation

Recent efforts in biometrics have focused on cross-domain face recognition where images from one domain are either transformed or synthesized. In this work, we focus on a similar problem for cross spectral periocular recognition where the images from Near Infra Red (NIR) domain are matched against Visible (VIS) spectrum images. Specifically, we propose to adapt a cascaded image transformation network that can produce NIR image given a VIS image. The proposed approach is first validated with regards to the quality of the image produced by employing various quality factors. Second the applicability is demonstrated with images generated by the proposed approach. We employ a publicly available cross-spectral periocular image data of 240 unique periocular instances captured in 8 different capture sessions. We experimentally validate that the proposed image transformation scheme can produce NIR like images and also can be used with any existing feature extraction scheme. To this extent, we demonstrate the biometric applicability by using both hand-crafted and deep neural network based features under verification setting. The obtained EER of 0.7% indicates the suitability of proposed approach for image transformation from the VIS to the NIR domain.