Affective Movement Recognition Based on Generative and Discriminative Stochastic Dynamic Models

• Published in: IEEE transactions on human-machine systems Vol. 44; no. 4; pp. 454 - 467
• Main Authors: Samadani, Ali-Akbar, Gorbet, Rob, Kulic, Dana
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2014; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affective movement analysis; Applied sciences; Computational modeling; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data processing. List processing. Character string processing; dimensionality reduction; Dynamical systems; Dynamics; Exact sciences and technology; Feature extraction; Feature recognition; Frequency selective surfaces; generative and discriminative techniques; Hidden Markov models; Human; Information retrieval. Graph; Kinematics; Memory organisation. Data processing; Principal component analysis; Principal components analysis; Recognition; Representations; Software; stochastic modeling; Stochastic processes; Stochasticity; Support vector machines; Theoretical computing; Data analysis; Probabilistic approach; Motion estimation; Man machine system; stochastic modeling; Affective movement analysis; Emotion emotionality; generative and discriminative techniques; Modeling; Fisher information; Dimension reduction; dimensionality reduction; Vector space; User interface; Kinematics; Vector support machine; Hidden Markov model; Fisher test; Dynamic model; Principal component analysis
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2014.2310953

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.