Advances in computer processing power and emerging algorithms are allowing new ways of envisioning human computer interaction. This paper focuses on the development of a computing algorithm that uses audio and visual sensors to detect and track a user's affective state to aid computer decision making. Using our multi-stream fused hidden Markov model (MFHMM), we analyzed coupled audio and visual streams to detect 11 cognitive/emotive states. The MFHMM allows the building of an optimal connection among multiple streams according to the maximum entropy principle and the maximum mutual information criterion. Person-independent experimental results from 20 subjects in 660 sequences show that the MFHMM approach performs with an accuracy of 80.61% which outperforms face-only HMM, pitch-only HMM, energy-only HMM, and independent HMM fusion.


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    Titel :

    Audio-visual affect recognition through multi-stream fused HMM for HCI


    Beteiligte:
    Zeng, Z. (Autor:in) / Tu, J. (Autor:in) / Pianfetti, B. (Autor:in) / Liu, M. (Autor:in) / Zhang, T. (Autor:in) / Zhang, Z. (Autor:in) / Huang, T.S. (Autor:in) / Levinson, S. (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    166253 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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