An Empirical Study of Face Recognition under Variations

Face recognition (FR) has recently made remark- able progress given the extraordinary capabilities of modern deep learning(DL) models. Though superior performance of DL based methods over human has been reported on benchmark dataset, it remains an open problem how those systems work in real-world condition with variations such as head pose, lighting, occlusion and image noises, in particular, in comparison to conventional FR approaches. It is hard to answer this question in a quantitative manner as current benchmark datasets are in lack of full range of variations. In this paper we propose a flexible approach to simulate face images under different variations with controllable degrees and based on the simulated dataset we quantitatively study how modern DL based and conventional FR methods perform. Based on the observations on a large number of synthesized face images, we draw several conclusions such as how head pose pitch and yaw variations will influence the FR systems and which part on the face is the most significant region, and in which situation conventional methods still show some advantages. The findings will not only be useful to assess current FR system in a quantitative manner but also shed light on future FR system design and data augmentation.