|dc.description.abstract||Body movements are an important non-verbal communication medium through which affective states of the demonstrator can be discerned. For machines, the capability to recognize affective expressions of their users and generate appropriate actuated responses with recognizable affective content has the potential to improve their life-like attributes and to create an engaging, entertaining, and empathic human-machine interaction.
This thesis develops approaches to systematically identify movement features most salient to affective expressions and to exploit these features to design computational models for automatic recognition and generation of affective movements. The proposed approaches enable 1) identifying which features of movement convey affective expressions, 2) the automatic recognition of affective expressions from movements, 3) understanding the impact of kinematic embodiment on the perception of affective movements, and 4) adapting pre-defined motion paths in order to "overlay" specific affective content.
Statistical learning and stochastic modeling approaches are leveraged, extended, and adapted to derive a concise representation of the movements that isolates movement features salient to affective expressions and enables efficient and accurate affective movement recognition and generation. In particular, the thesis presents two new approaches to fixed-length affective movement representation based on 1) functional feature transformation, and 2) stochastic feature transformation (Fisher scores). The resulting representations are then exploited for recognition of affective expressions in movements and for salient movement feature identification. For functional representation, the thesis adapts dimensionality reduction techniques (namely, principal component analysis (PCA), Fisher discriminant analysis, Isomap) for functional datasets and applies the resulting reduction techniques to extract a minimal set of features along which affect-specific movements are best separable. Furthermore, the centroids of affect-specific clusters of movements in the resulting functional PCA subspace along with the inverse mapping of functional PCA are used to generate prototypical movements for each affective expression.
The functional discriminative modeling is however limited to cases where affect-specific movements also have similar kinematic trajectories and does not address the interpersonal and stochastic variations inherent to bodily expression of affect. To account for these variations, the thesis presents a novel affective movement representation in terms of stochastically-transformed features referred to as Fisher scores. The Fisher scores are derived from affect-specific hidden Markov model encoding of the movements and exploited to discriminate between different affective expressions using a support vector machine (SVM) classification. Furthermore, the thesis presents a new approach for systematic identification of a minimal set of movement features most salient to discriminating between different affective expressions. The salient features are identified by mapping Fisher scores to a low-dimensional subspace where dependencies between the movements and their affective labels are maximized. This is done by maximizing Hilbert Schmidt independence criterion between the Fisher score representation of movements and their affective labels. The resulting subspace forms a suitable basis for affective movement recognition using nearest neighbour classification and retains the high recognition rates achieved by SVM classification in the Fisher score space. The dimensions of the subspace form a minimal set of salient features and are used to explore the movement kinematic and dynamic cues that connote affective expressions.
Furthermore, the thesis proposes the use of movement notation systems from the dance community (specifically, the Laban system) for abstract coding and computational analysis of movement. A quantification approach for Laban Effort and Shape is proposed and used to develop a new computational model for affective movement generation. Using the Laban Effort and Shape components, the proposed generation approach searches a labeled dataset for movements that are kinematically similar to a desired motion path and convey a target emotion. A hidden Markov model of the identified movements is obtained and used with the desired motion path in the Viterbi state estimation. The estimated state sequence is then used to generate a novel movement that is a version of the desired motion path, modulated to convey the target emotion.
Various affective human movement corpora are used to evaluate and demonstrate the efficacy of the developed approaches for the automatic recognition and generation of affective expressions in movements.
Finally, the thesis assesses the human perception of affective movements and the impact of display embodiment and the observer's gender on the affective movement perception via user studies in which participants rate the expressivity of synthetically-generated and human-generated affective movements animated on anthropomorphic and non-anthropomorphic embodiments. The user studies show that the human perception of affective movements is mainly shaped by intended emotions, and that the display embodiment and the observer's gender can significantly impact the perception of affective movements.||en