modeling facial expressions and peripheral physiological signals to predict topical relevance
By analyzing explicit & implicit feedback information retrieval systems can determine topical relevance and tailor search criteria to the user's needs. In this paper we investigate whether it is possible to infer what is relevant by observing user affective behaviour. The sensory data employed range between facial expressions and peripheral physiological signals. We extract a set of features from the signals and analyze the data using classification methods, such as SVM and KNN. The results of our initial evaluation indicate that prediction of relevance is possible, to a certain extent, and implicit feedback models can benefit from taking into account user affective behavior.
classification multimedia retrieval physiological signal processing pattern recognition facial expression analysis affective feedback support vector machines