Abstract:
Deep Convolutional Neural Networks (CNNs) have achieved remarkable progress in the field of face recognition (FR). However, developing a robust FR system in the real-world is still challenging due to vast variance of illumination, visual quality, and camera angles in different scenarios. These factors may result in significant accuracy drop, if the pretrained model doesn’t have perfect generalization ability. To mitigate this issue, we present a solution named SAFACE, which helps to improve FR accuracy through unsupervised online-learning in an edge computing system. Specifically, we propose a novel scenario-aware FR flow, then decouple the flow into different phases and map each of them to different levels of a three-layer edge computing system. For evaluation, we implement a prototype and demonstrate its advantages in both improving recognition accuracy and reducing processing latency.
