Audio-Visual Classification and Fusion of Spontaneous Affective Data in Likelihood Space
This paper focuses on audio-visual (using facial expression, shoulder and audio cues) classification of spontaneous affect, utilising generative models for classification (i) in terms of Maximum Likelihood Classification with the assumption that the generative model structure in the classifier is correct, and (ii) Likelihood Space Classification with the assumption that the generative model structure in the classifier may be incorrect, and therefore, the classification performance can be improved by projecting the results of generative classifiers onto likelihood space, and then using discriminative classifiers. Experiments are conducted by utilising Hidden Markov Models for single cue classification, and 2 and 3-chain coupled Hidden Markov Models for fusing multiple cues and modalities. For discriminative classification, we utilise Support Vector Machines. Results show that Likelihood Space Classification improves the performance (91.76%) of Maximum Likelihood Classification (79.1%). Thereafter, we introduce the concept of fusion in the likelihood space, which is shown to outperform the typically used model-level fusion, attaining a classification accuracy of 94.01% and further improving all previous results.