Dynamic emotional memory analysis in digital animation via expression recognition and scene atmosphere enhancement

Abstract

Digital animation serves as a crucial medium for conveying emotions and values. To grasp the emotional perspectives within digital animation, this paper introduces an emotional memory analysis approach grounded in dynamic features. Initially, leveraging the bidirectional alignment mechanism, a CNN-based expression recognition system is proposed to extract the expressive information of characters in the animation. Subsequently, a sentiment analysis technique tailored for enhancing single-frame animation scenes is presented, addressing the issue of atmosphere intensification in these scenes. Ultimately, by integrating expression information and atmosphere data, a sentiment analysis method focused on dynamic features is suggested to establish the emotional relationship between frames, thereby deriving the emotional value of digital animation. Experiments can conclude that our method can obtain the excellent results which are better than state-of-the-art and can realize the emotional polarity analysis of digital animation content.