Modeling Latent Discriminative Dynamic of Multi-Dimensional Affective Signals
Abstract
During face-to-face communication, people continuously exchange para-linguistic information such as their emotional state through facial expressions, posture shifts, gaze patterns and prosody. These affective signals are subtle and complex. In this paper, we propose to explicitly model the interaction between the high level perceptual features using Latent-Dynamic Conditional Random Fields. This approach has the advantage of explicitly learning the sub-structure of the affective signals as well as the extrinsic dynamic between emotional labels. We evaluate our approach on the Audio-Visual Emotion Challenge (AVEC2011) dataset. By using visual features easily computable using off-the shelf sensing software (vertical and horizontal eye gaze, head tilt and smile intensity), we show that our approach based on LDCRF model outperforms previously published baselines for all four affective dimensions. By integrating audio features, our approach also outperforms the audio-visual baseline.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2011
- Accession Number
- AD1171329
Entities
People
- Geovany A Ramirez
- Louis-Philippe Morency
- Tadas Baltrusaitis
Organizations
- University of Southern California