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Although modern face verification systems are accessible and accurate, theyare not always robust to pose variance and occlusions. Moreover, accuratemodels require a large amount of data to train. We structure our experiments tooperate on small amounts of data obtained from an NGO that funds ophthalmicsurgeries. We set up our face verification task as that of verifyingpre-operation and post-operation images of a patient that undergoes ophthalmicsurgery, and as such the post-operation images have occlusions like an eyepatch. In this paper, we present a system that performs the face verificationtask using one-shot learning. To this end, our paper uses deep convolutionalnetworks and compares different model architectures and loss functions. Ourbest model achieves 85% test accuracy. During inference time, we also attemptto detect image forgeries in addition to performing face verification. Toachieve this, we use Error Level Analysis. Finally, we propose an inferencepipeline that demonstrates how these techniques can be used to implement anautomated face verification and forgery detection system.

Face Verification and Forgery Detection for Ophthalmic Surgery Images