2020/06/22 by Alexandra Lindt, Lindt, Alexandra, Pablo Barros +5 · 2 citations
Computer Science · Engineering · Psychology · #Computer Vision and Pattern Recognition (cs.CV) #Emotion and Mood Recognition #FOS: Computer and information sciences #FOS: Electrical engineering #Face recognition and analysis #Generative Adversarial Networks and Image Synthesis #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2006.12210
8 pages, 5 figures. 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019), May 2019
arxiv created 2020/06/22 · openalex publication_date 2020/06/22 · arxiv updated 2020/06/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
Recently deep generative models have achieved impressive results in the field of automated facial expression editing. However, the approaches presented so far presume a discrete representation of human emotions and are therefore limited in the modelling of non-discrete emotional expressions. To overcome this limitation, we explore how continuous emotion representations can be used to control automated expression editing. We propose a deep generative model that can be used to manipulate facial expressions in facial images according to continuous two-dimensional emotion labels. One dimension represents an emotion's valence, the other represents its degree of arousal. We demonstrate the functionality of our model with a quantitative analysis using classifier networks as well as with a qualitative analysis.