Video emotion recognition is an important branch of affective computing, and
its solutions can be applied in different fields such as human-computer
interaction (HCI) and intelligent medical treatment. Although the number of
papers published in the field of emotion recognition is increasing, there are
few comprehensive literature reviews covering related research on video emotion
recognition. Therefore, this paper selects articles published from 2015 to 2023
to systematize the existing trends in video emotion recognition in related
studies. In this paper, we first talk about two typical emotion models, then we
talk about databases that are frequently utilized for video emotion
recognition, including unimodal databases and multimodal databases. Next, we
look at and classify the specific structure and performance of modern unimodal
and multimodal video emotion recognition methods, talk about the benefits and
drawbacks of each, and then we compare them in detail in the tables. Further,
we sum up the primary difficulties right now looked by video emotion
recognition undertakings and point out probably the most encouraging future
headings, such as establishing an open benchmark database and better multimodal
fusion strategys. The essential objective of this paper is to assist scholarly
and modern scientists with keeping up to date with the most recent advances and
new improvements in this speedy, high-influence field of video emotion
recognition