  Although modern face verification systems are accessible and accurate, they
are not always robust to pose variance and occlusions. Moreover, accurate
models require a large amount of data to train. We structure our experiments to
operate on small amounts of data obtained from an NGO that funds ophthalmic
surgeries. We set up our face verification task as that of verifying
pre-operation and post-operation images of a patient that undergoes ophthalmic
surgery, and as such the post-operation images have occlusions like an eye
patch. In this paper, we present a system that performs the face verification
task using one-shot learning. To this end, our paper uses deep convolutional
networks and compares different model architectures and loss functions. Our
best model achieves 85% test accuracy. During inference time, we also attempt
to detect image forgeries in addition to performing face verification. To
achieve this, we use Error Level Analysis. Finally, we propose an inference
pipeline that demonstrates how these techniques can be used to implement an
automated face verification and forgery detection system.
