Name
Title | ||
|---|---|---|
Multimodal Recognition Of Emotions Using Physiological Signals With The Method Of Decision-Level Fusion For Healthcare Applications |
Abstract | ||
|---|---|---|
Automatic emotion recognition enhance dramatically the development of human/machine dialogue. Indeed, it allows computers to determine the emotion felt by the user and adapt consequently its behavior. This paper presents a new method for the fusion of signals for the purpose of a multimodal recognition of eight basic emotions using physiological signals. After a learning phase where an emotion data base is constructed, we apply the recognition algorithm on each modality separately. Then, we merge all these decisions separately by applying a decision fusion approach to improve recognition rate. The experiments show that the proposed method allows high accuracy emotion recognition. Indeed we get a recognition rate of 81.69% under some conditions. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-19312-0_26 | Inclusive Smart Cities and e-Health |
Keywords | Field | DocType |
Signal fusion method, Basic emotions, Multimodal detection, Physiological signals | Decision level,Decision fusion,Emotion recognition,Computer science,Emotion classification,Speech recognition,Recognition algorithm,Merge (version control) | Conference |
Volume | ISSN | Citations |
9102 | 0302-9743 | 2 |
PageRank | References | Authors |
0.46 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chaka Koné | 1 | 2 | 0.46 |
| Imen Tayari Meftah | 2 | 4 | 1.55 |
| Nhan Le Thanh | 3 | 28 | 14.98 |
| Cécile Belleudy | 4 | 84 | 12.98 |