FACE RECOGNITION FOR FISHEYE IMAGES

Face recognition often suffers from severe degradation in accuracy when applied to images captured by fisheye cameras. One way to resolve the issue is to rectify the fisheye images before classification; however, it can only achieve local optimum. In this paper, we present an end-to-end model with global optimum. To tackle the challenges due to intra-class variance and diversity of fisheye transformations, we propose 1) a structural correction to guide the model learning, and 2) a spatial-transformer-networks embedded model to compensate for the non-linear distortion of fisheye lenses. We test the proposed model on the CelebA dataset and a real image dataset and achieve an average accuracy of 98.7% and 98.0%, respectively, which represent improvements of 4.05% and 5.72% over the state-of-the-art results.