Personalized Frame-Level Facial Expression Recognition in Video

Andrey V. Savchenko

Abstract: In this paper, the personalization of the video-based frame-level facial expression recognition is studied for multi-user systems if a small amount of short videos are available for each user. At first, embeddings of each video frame are computed using deep convolutional neural network pre-trained on a large emotional dataset of static images. Next, a dataset of videos is used to train a subject-independent emotion classifier, such as feed-forward neural network or frame attention network. Finally, it is proposed to fine-tune this neural classifier on the videos of each user of interest. As a result, every user is associated with his or her own emotional model. The classifier in a multi-user system is chosen by an appropriate video-based face recognition method. The experimental study with the RAMAS dataset demonstrates the significant (up to 25%) increase in accuracy of the proposed approach when compared to a subject-independent facial expression recognition.