Real-Time Facial Expression Recognition on Smartphones

Abstract

Temporal segmentation of real time video is an important part for automatic facial expression recognition system. Many studies for facial expression recognition have been carried out under restricted experimental environment such as pre-segmented video set. In this paper, we present a real-time temporal video segmenting approach for automatic facial expression recognition applicable in a smartphone. The proposed system uses a Finite State Machine (FSM) for segmenting real time video into temporal phases from neutral expression to the peak of an expression. The FSM uses Lucas-Kanade's optical flow vector based scores for state transitions to adapt the varying speeds of facial expressions. While even HMM based or hybrid HMM model based approaches handling time series data require sampling times, the proposed system runs without any sampling time delay. The proposed system performs facial expression recognition with Support Vector Machines (SVM) on every apex state after automatic temporal segmentation. The mobile app with our approach runs on Samsung Galaxy S3 with 3.7 fps and the accuracy of real-time mobile emotion recognition is about 70.6% for 6 basic emotions by 5 subjects who are not professional actors.