Emotion inference from human body motion

Most efforts to recognise emotions from the human body have focused on expressive gestures which are archetypal and exaggerated expressions of emotions. The principal contribution of this dissertation is the influence of emotional states from everyday actions such as walking, knocking and throwing. The implementation of the system draws inspiration from a variety of disciplines including psychology, character animation and speech recognition. Complex actions are modelled using Hidden Markov Models and motion primitives. This dissertation describes a holistic approach which models emotional, action and personal influences in order to maximise the discriminability of different emotion classes. A pipeline is developed which incrementally removes the biases introduced by different action contexts and individual differences. The resulting signal is described in terms of posture and dynamic features and classified into one of several emotion classes using statistically trained Support Vector Machines. The system also goes beyond isolated expressions and is able to classify natural action sequences. I use Level Building to segment action sequences and combine component classifications using an incremental voting scheme which is suitable for online applications. The system is comprehensively evaluated along a number of dimensions using a corpus of motion-captured actions. For isolated actions I evaluate the generalisation performance to new subjects. For action sequences I study the effects of reusing models trained on the isolated cases vs. adapting models to connected samples. The dissertation also evaluates the role of modelling the influence of individual user differences.EThOS - Electronic Theses Online ServiceGBUnited Kingdo