Person Identification using Periocular Region

The world has been affected by the corona virus epidemic, which soon turned into a pandemic. The need for face masks and social isolation highlight the need for contact-free biometric authentication for all future systems. Periocular biometric, which does not require physical contact is a solution in such scenarios because it can detect people wearing face masks. The term "periocular region" describes the region around the eye, which includes a variety of elements such the sclera, eyelids, lashes, brows, and skin. Our method involves extraction of the region of interest by cropping the periocular region of every facial image. CLAHE is then performed on each periocular image extracted. For feature extraction, VGG16 and ResNet50 CNN models are employed. KNN classifiers are used for classification of the image. Our experiments demonstrate that the proposed approach achieves an accuracy of 80% with ResNet50 and 75% with VGG16 on a challenging dataset consisting of 24 subjects with significant variations in pose, illumination, and expression. The proposed approach offers a promising solution for noninvasive and contactless biometric identification, which can have broad applications in security and surveillance systems.