Static vs. dynamic modeling of human nonverbal behavior from multiple cues and modalities

Human nonverbal behavior recognition from multiple cues and modalities has attracted a lot of interest in recent years. Despite the interest, many research questions, including the type of feature representation, choice of static vs. dynamic classification schemes, the number and type of cues or modalities to use, and the optimal way of fusing these, remain open research questions. This paper compares frame-based vs. window-based feature representation and employs static vs. dynamic classification schemes for two distinct problems in the field of automatic human nonverbal behavior analysis: multicue discrimination between posed and spontaneous smiles from facial expressions, head and shoulder movements, and audio-visual discrimination between laughter and speech. Single cue and single modality results are compared to multicue and multimodal results by employing Neural Networks, Hidden Markov Models (HMMs), and 2and 3-chain coupled HMMs. Subject independent experimental evaluation s...

• Type: Conference
• Year: 2009
• Where: ICMI
• Authors: Stavros Petridis, Hatice Gunes, Sebastian Kaltwang, Maja Pantic
• Keywords: Biometrics, Dynamic Classification, Feature Representation, Human Nonverbal Behavior, ICMI 2009