Abstract
Recent advances in human-computer interaction technology go beyond the successful transfer of data between human and machine by seeking to improve the naturalness and friendliness of user interactions. An important augmentation, and potential source of feedback, comes from recognizing the user‘s expressed emotion or affect. This chapter presents an overview of research efforts to classify emotion using different modalities: audio, visual and audio-visual combined. Theories of emotion provide a framework for defining emotional categories or classes. The first step, then, in the study of human affect recognition involves the construction of suitable databases. The authorsdescribe fifteen audio, visual and audio-visual data sets, and the types of feature that researchers have used to represent the emotional content. They discuss data-driven methods of feature selection and reduction, which discard noise and irrelevant information to maximize the concentration of useful information. They focus on the popular types of classifier that are used to decide to which emotion class a given example belongs, and methods of fusing information from multiple modalities. Finally, the authors point to some interesting areas for future investigation in this field, and conclude.