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 consensus 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2010
- Accession Number
- AD1170847
Entities
People
- Derya Ozkan
- Louis-phillippe Morency
Organizations
- University of Southern California