Abstract
Effective facial expression recognition is a very important part of perceiving the user's emotions and designing a successful human-computer interaction system in all fields. Many studies also applied computer vision, machine learning, and deep learning methods on affective computing. However, they mainly focus on the facial expression identification by a single static image, but didn't consider the continuous facial expression of human emotions maybe is for a period of times. Therefore, to recognize the pattern of continuous facial expression and mood changes for further application is still a challenging task. In this study, we propose a continuous facial expression recognition model based on deep learning approach, which combined convolutional neural networks (CNN) and recurrent neural networks (RNN) to analyze and identify continuous facial expressions in a period of time to improve the traditional image recognition method.
Published Version
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