Concensus of Self-features for Nonverbal Behavior Analysis

Abstract. One of the key challenge in social behavior analysis is to automatically discover the subset of features relevant to a specific social signal (e.g., backchannel feedback). The way that these social signals are performed exhibit some variations among different people. In this paper, we present a feature selection approach which first looks at important behaviors for each individual, called self-features, before building a consensus. To enable this approach, we propose a new feature ranking scheme which exploits the sparsity of probabilistic models when trained on human behavior problems. We validated our self-feature concensus approach on the task of listener backchannel prediction and showed improvement over the traditional group-feature approach. Our technique gives researchers a new tool to analyze individual differences in social nonverbal communication. Key words: Feature selection, non-verbal behavior analysis, L1 regularization.

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Derya Ozkan, Louis-Philippe Morency
• Keywords: Behavior, Computer Vision, Feature Selection, ICPR 2010, Social Signals