  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.
