Face Verification and Forgery Detection for Ophthalmic Surgery Images
This addresses verification challenges in medical settings with limited data, but it is incremental as it applies existing methods to a specific domain.
The paper tackles face verification for ophthalmic surgery images with occlusions like eye patches, using one-shot learning with deep convolutional networks, achieving 85% test accuracy, and integrates forgery detection via Error Level Analysis.
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.