Affective movement recognition based on generative and discriminative stochastic dynamic models

For an engaging human-machine interaction, machines need to be equipped with affective communication abilities. Such abilities enable interactive machines to recognize the affective expressions of their users, and respond appropriately through different modalities including movement. This paper focuses on bodily expressions of affect, and presents a new computational model for affective movement recognition, robust to kinematic, interpersonal, and stochastic variations in affective movements. The proposed approach derives a stochastic model of the affective movement dynamics using hidden Markov models (HMMs). The resulting HMMs are then used to derive a Fisher score representation of the movements, which is subsequently used to optimize affective movement recognition using support vector machine classification. In addition, this paper presents an approach to obtain a minimal discriminative representation of the movements using supervised principal component analysis (SPCA) that is based on Hilbert-Schmidt independence criterion in the Fisher score space. The dimensions of the resulting SPCA subspace consist of intrinsic movement features salient to affective movement recognition. These salient features enable a low-dimensional encoding of observed movements during a human-machine interaction, which can be used to recognize and analyze human affect that is displayed through movement. The efficacy of the proposed approach in recognizing affective movements and identifying a minimal discriminative movement representation is demonstrated using two challenging affective movement datasets.