Name
Title | ||
|---|---|---|
Being bored? Recognising natural interest by extensive audiovisual integration for real-life application |
Abstract | ||
|---|---|---|
Automatic detection of the level of human interest is of high relevance for many technical applications, such as automatic customer care or tutoring systems. However, the recognition of spontaneous interest in natural conversations independently of the subject remains a challenge. Identification of human affective states relying on single modalities only is often impossible, even for humans, since different modalities contain partially disjunctive cues. Multimodal approaches to human affect recognition generally are shown to boost recognition performance, yet are evaluated in restrictive laboratory settings only. Herein we introduce a fully automatic processing combination of Active-Appearance-Model-based facial expression, vision-based eye-activity estimation, acoustic features, linguistic analysis, non-linguistic vocalisations, and temporal context information in an early feature fusion process. We provide detailed subject-independent results for classification and regression of the Level of Interest using Support-Vector Machines on an audiovisual interest corpus (AVIC) consisting of spontaneous, conversational speech demonstrating ''theoretical'' effectiveness of the approach. Further, to evaluate the approach with regards to real-life usability a user-study is conducted for proof of ''practical'' effectiveness. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.imavis.2009.02.013 | Image Vision Comput. |
Keywords | Field | DocType |
automatic detection,real-life application,human affective state,affective computing,natural interest,recognition performance,human affect recognition,automatic processing combination,spontaneous interest,automatic customer care,audiovisual processing,audiovisual interest corpus,human interest,interest recognition,active-appearance-model-based facial expression,extensive audiovisual integration,facial expression,support vector machine,active appearance model | Modalities,Computer vision,Usability,Facial expression,Artificial intelligence,Temporal context,Automatic processing,Affective computing,Affect (psychology),Mathematics,Linguistic analysis | Journal |
Volume | Issue | ISSN |
27 | 12 | Image and Vision Computing |
Citations | PageRank | References |
80 | 4.56 | 41 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Björn Schuller | 1 | 6749 | 463.50 |
| Ronald Müller | 2 | 174 | 11.03 |
| Florian Eyben | 3 | 2854 | 141.87 |
| Jürgen Gast | 4 | 92 | 6.90 |
| Benedikt Hörnler | 5 | 120 | 9.61 |
| Martin Wöllmer | 6 | 1359 | 81.78 |
| Gerhard Rigoll | 7 | 2788 | 268.87 |
| Anja Höthker | 8 | 86 | 5.41 |
| Hitoshi Konosu | 9 | 136 | 10.46 |