Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/FG.2019.8756558 | 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019) |
Keywords | Field | DocType |
automated facial expression editing,deep generative model,facial images,continuous emotion labels,human emotion representation,generative adversarial networks,deep convolutional GAN | Arousal,Pattern recognition,Computer science,Facial expression,Emotional expression,Artificial intelligence,Generative grammar,Classifier (linguistics),Discrete representation,Generative model | Conference |
ISSN | ISBN | Citations |
2326-5396 | 978-1-7281-0090-6 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandra Lindt | 1 | 1 | 0.35 |
| Pablo V. A. Barros | 2 | 119 | 22.02 |
| Henrique Siqueira | 3 | 2 | 3.40 |
| Stefan Wermter | 4 | 1100 | 151.62 |