TY - JFULL AU - Achut Manandhar and Kenneth D. Morton and Peter A. Torrione and Leslie M. Collins PY - 2016/4/ TI - Multivariate Output-Associative RVM for Multi-Dimensional Affect Predictions T2 - International Journal of Computer and Information Engineering SP - 460 EP - 468 VL - 10 SN - 1307-6892 UR - https://publications.waset.org/pdf/10003819 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 111, 2016 N2 - 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. ER -