Abstract. In the application of face recognition, eyeglasses could significantly degrade the recognition accuracy. A feasible method is to collect large-scale face images with eyeglasses for training deep learning methods. However, it is difficult to collect the images with and without glasses of the same identity, so that it is difficult to optimize the intra-variations caused by eyeglasses. In this paper, we propose to address this problem in a virtual synthesis manner. The high-fidelity face images with eyeglasses are synthesized based on 3D face model and 3D eyeglasses. Models based on deep learning methods are then trained on the synthesized eyeglass face dataset, achieving better performance than previous ones. Experiments on the real face database validate the effectiveness of our synthesized data for improving eyeglass face recognition performance.
Similarly, Guo et al. REF synthesize high-fidelity face images with eyeglasses as training data based on 3D face model and 3D eyeglasses and achieve better face recognition performance on real eyeglass face testset.
Face Synthesis for Eyeglass-Robust Face Recognition
ArXiv
ArXiv
Computer Science