Weighted comparison score fusion for accurate verification of surgically altered periocular region

Facial surgeries are inevitably increasing in the modern society due to factors such as consciousness for beauty/appearance either to correct the deformed structure as a result of the injury, trauma or natural process of aging. These surgical changes in the facial structure result in modified appearance of a person, which cannot be modelled using morphological or mathematical approaches. These changes directly impact the biometric performance of a recognition system, when a subject is enrolled prior to surgical modifications and is verified after the surgery. Emphasizing on the periocular region as a stand-alone biometric characteristic is on one hand increasing while on the other hand, in the target population the number of surgeries for periocular region is also increasing. These surgical changes have a direct negative impact on the biometric performance. In this work, we evaluate the degraded performance as a result of such surgeries in the periocular region by enrolling the 402 unique periocular images prior to surgery and verify mated images post-surgery by employing various well-known feature extraction techniques. The obtained verification performance equals GMR of 85.32% at a FMR of 0.01% indicating the challenge for a real-life application. Further, we explore weighted comparison score level fusion in which the weights are determined using Fischer Discriminant Ratio. The proposed weighted score level fusion in this work provides an improved verification performance of GMR = 100% at FMR = 0.01% signifying the robust nature of fused scores as compared to state-of-art verification schemes in handling the challenging data arising due to surgical modifications.