Visible Light , BiModal Ocular Biometrics

Most current ocular biometric systems are based on recognition and classification of unique iridial patterns typically captured in near infrared spectrum (NIR). However, eye images in the visible spectrum (standard RGB images) also reveal some iridial patterns in addition to the vascularity of the white of the eyes (Conjunctival vasculature on the sclera). Conjunctival vascular patterns have rich and uniquely identifiable information. The combination of the aforementioned modalities provides an opportunity for bi-modal ocular biometrics using only visible spectrum captures. Multimodality improves the performance of biometric systems compared to single mode biometrics. In this work, a bi-modal ocular biometric system is described that fuses information from conjunctival vasculature and iris modalities. We report the performance of this enhanced complementary biometric identification using UBIRIS v1 database. The match score level fusion is performed and the proposed method produces a receiver operating characteristic area under the curve (AUC) of 0.9954 and an equal error rate (EER) of 0.0452 for a quality-vetted subset of UBIRIS V1. In comparison, individual modalities yield an AUC of 0.9822 with an EER of 0.0759 for iris and an AUC of 0.9623 with an EER of 0.1022 for conjunctival vasculature. The proposed new algorithms may overcome constraint of using NIR imaging for iris and provide a better overall performance using only RGB images. We conclude that bi-modal iris –conjunctival fusion can improve the otherwise challenging RGB recognition of iris. © 2011 Published by Elsevier Ltd