Rapid Synthesis of Massive Face Sets for Improved Face Recognition

Recent work demonstrated that computer graphics techniques can be used to improve face recognition performances by synthesizing multiple new views of faces available in existing face collections. By so doing, more images and more appearance variations are available for training, thereby improving the deep models trained on these images. Similar rendering techniques were also applied at test time to align faces in 3D and reduce appearance variations when comparing faces. These previous results, however, did not consider the computational cost of rendering: At training, rendering millions of face images can be prohibitive; at test time, rendering can quickly become a bottleneck, particularly when multiple images represent a subject. This paper builds on a number of observations which, under certain circumstances, allow rendering new 3D views of faces at a computational cost which is equivalent to simple 2D image warping. We demonstrate this by showing that the run-time of an optimized OpenGL rendering engine is slower than the simple Python implementation we designed for the same purpose. The proposed rendering is used in a face recognition pipeline and tested on the challenging IJB-A and Janus CS2 benchmarks. Our results show that our rendering is not only fast, but improves recognition accuracy.