Name
Abstract | ||
|---|---|---|
We describe a method for dynamic emotion recognition from facial expression sequences. Our model is based on learning a latent space using the Gaussian Process Latent Variable Model (GP-LVM), encapsulating facial landmarks shapes which describe a given facial expression. We incorporate the dynamic model by learning the latent representation, with the aim to respect the data's dynamics (facial shapes should maintain their correspondence along time). Then, a Gaussian process classifier is implemented to evaluate the relevance of the latent space features in the emotion recognition task. The results show that the proposed method can efficiently model a dynamic facial emotion and recognize with high accuracy a facial emotion sequence. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-14364-4_77 | ADVANCES IN VISUAL COMPUTING (ISVC 2014), PT II |
Field | DocType | Volume |
Computer vision,Facial expression recognition,Pattern recognition,Gaussian process latent variable model,Computer science,Emotion recognition,Latent variable model,Facial expression,Artificial intelligence,Gaussian process,Classifier (linguistics) | Conference | 8888 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.37 |
References | Authors | |
20 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hernán F. García | 1 | 6 | 3.62 |
| Mauricio A. Álvarez | 2 | 165 | 23.80 |
| Álvaro Á. Orozco | 3 | 16 | 12.88 |