A novel periocular biometrics solution for authentication during Covid-19 pandemic situation.

The outbreak of novel coronavirus in 2019 has shaken the whole world and it quickly evolved as a global pandemic, placing everyone in a panic situation. Considering its long-term effects on day to day lives, the necessity of wearing face mask and social distancing brings in picture the requirement of a contact less biometric system for all future authentication systems. One of the solutions is to use periocular biometric as it does not need physical contact like fingerprint biometric and is able to identify even people wearing face masks. Since, the periocular region is a small area as compared to face, extraction of required number of features from that small region is the major concern to make the system highly robust. This research proposes a feature fusion approach which combines the handcrafted features HOG, non-handcrafted features extracted using pretrained CNN models and gender related features extracted using a five layer CNN model. The proposed feature fusion approach is evaluated using multiclass SVM classifier with three different benchmark databases, UBIPr, Color FERET and Ethnic Ocular as well as for three non-ideal scenarios i.e. the effect of eyeglasses, effect of eye occlusion and pose variations. The proposed approach shows remarkable improvement in performance over pre-existing approaches.