基于分布式边缘计算的情绪识别系统

Emotion Recognition System Based on Distributed Edge Computing

近年来,边缘计算和人工智能结合的模式越来越流行。面部动作单元(ActionUnit)检测分析是一种通过分析局部面部区域中某些原子肌肉运动的线索来识别面部表情的方法。根据面部特征点的检测,可以计算出AU的值,然后通过对这些AU值进行分类来进行实时情绪检测。然而,在实际的生产过程中,由于传输面部动作单元特征数据网络的开销巨大,这会给在生产中的通信网络带来新的挑战,因此可以选择使用树莓派,实验中设计了基于轻量级边缘计算的分布式系统,优化了数据传输和组件部署。将部分计算任务转移到服务器附近,前端和后端处理模式分开可以有效缩短往返延迟,从而完成复杂的计算任务,并提高可靠性,大规模连接服务。

In recent years,the combination of edge computing and artificial intelligence has become more and more popular.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 values of AU,and then use classification algorithms for emotion recognition.However,in the actual production process,due to the tremendous network overhead of transferring the facial action unit feature data,it poses new challenges of this system being deployed in a distributed manner while running in production.Therefore,we design a lightweight edge computing based distributed system using Raspberry Pi tailed for this need,and optimize the data transfer and components deployment.In the vicinity,the front-end and back-end processing modes are separated to reduce round-trip delay,thereby completing complex computing tasks and providing high-reliability,large-scale connection services.