Facial Expression Recognition System on a Distributed Edge-Cloud Infrastructure

Abstract

Time-sensitive AI applications usually pre-process the raw data on edge devices without having to offload them all to the cloud. However, deploying the AI applications on a distributed edge-cloud infrastructure is still an open issue since separating the roles between the edge and the cloud has no existing rule to follow. In this paper, we implemented a Facial Expression Recognition (FER) system, as a case study AI application, on an edge-cloud infrastructure to bridge the gap. FER system is distributed, fault tolerant, performant and completely edge-cloud separated. FER performs light-weight algorithms such as extracting facial feature points on the edge, while it performs heavy-weight algorithms such as deep neural network inference on the cloud. We performed experiments on different cloud providers, and we have seen that we reduced the network overhead significantly and improved the performance by 25% compared with deploying it solely on the cloud, with only the feature data being transferred to the cloud instead of all the raw data.