Probabilistic Combination of Multiple Modalities to Detect Interest

This paper describes a new approach to combine multiple modalities and applies it to the problem of affect recognition. The problem is posed as a combination of classifiers in a probabilistic framework that naturally explains the concepts of experts and critics. Each channel of data has an expert associated that generates the beliefs about the correct class. Probabilistic models of error and the critics, which predict the performance of the expert on the current input, are used to combine the expert's beliefs about the correct class. The method is applied to detect the affective state of interest using information from the face, postures and task the subjects are performing. The classification using multiple modalities achieves a recognition accuracy of 67.8%, outperforming the classification using individual modalities. Further, the proposed combination scheme achieves the greatest reduction in error when compared with other classifier combination methods.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: Ashish Kapoor, Rosalind W. Picard, Yuri Ivanov
• Keywords: Classifier Combination Methods, Computer Vision, Correct Class, ICPR 2004, Multiple Modalities