EmotionTracker: A Mobile Real-time Facial Expression Tracking System with the Assistant of Public AI-as-a-Service

Abstract

Public AI-as-a-Service (AIaaS) is a promising next-generation computing paradigm that attracts resource-limited mobile users to outsource their machine learning tasks. However, the time delay between cloud/edge servers and end users makes it hard for real-time mobile artificial intelligence applications. In this demonstration, we present EmotionTracker, a real-time mobile facial expression tracking system combining AIaaS and mobile local auxiliary computing, including facial expression tracking and the corresponding task offloading. Mobile facial expression tracking iteratively estimates the facial expression with the help of sparse optical flow and neural network. Task offloading dynamically estimate the moment of task offloading with machine learning method. According to the results in a real-world environment, EmotionTracker successfully fulfills the mobile real-time facial expression tracking requirements.