Facial Expression Recognition Based on Edge Computing

Facial action unit (AU) detection recognizes facial expressions by analyzing cues about the movement of certain atomic muscles in the local facial area. According to the detection of facial feature points, we can calculate the value of AU, and then classification algorithms are performed on these AU values for emotion detection in realtime. When this advanced system is in the actual production process, large network bandwidth is usually required to transmit a large number of frames from cameras to the backend servers. To overcome this issue, we propose to offload the computing to edge devices in which the raw image data from each camera is directly processed with our optimized and customized algorithms, and then the detected emotions are transmitted to the end-user more easily compared with less data.