Automatic facial expression recognition based on MobileNetV2 in Real-time

Abstract

Facial expression recognition (FER) plays a vital role in human computer interaction and has become important filed of choice for researchers in computer vision and artificial intelligence over the last two decades. As we know, the background or non-face areas of image will seriously affect the accuracy of expression recognition. In the era of mobile networks, the demand for lightweight networks and real-time is growing. However, many expression recognition networks cannot meet the real-time requirements due to excessive parameters and calculations. In order to solve this problems, our proposed methodology combines a supervised transfer learning strategy and a joint supervision method with island loss, which is crucial for facial tasks. In addition, newly proposed Convolutional Neural Network (CNN) model, MobileNetv2, which has both accuracy and speed, is deployed in a real-time framework that enables fast and accurate real-time output. As a result, superior performance to other state-of-the-art methods is achieved in facial expression databases CK+, JAFFE and FER2013.