Work

Multivariate Output-Associative Rvm For Multi-Dimensional Affect Predictions

Year: 2016

Type: article

Abstract: The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.1111900

Authors: Achut Manandhar, Morton, Kenneth D., Torrione, Peter A., Collins, Leslie M.

Institution: Duke University

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: green

Funders: Research, Development and Engineering Command, Army Research Office

Awards: G7452299184, G7675225862, G855967382