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ICPR
2010
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Multi-Modal Emotion Recognition Using Canonical Correlations and Acoustic Features

13 years 11 months ago
Multi-Modal Emotion Recognition Using Canonical Correlations and Acoustic Features
The information of the psycho-physical state of the subject is becoming a valuable addition to the modern audio or video recognition systems. As well as enabling a better user experience, it can also assist in superior recognition accuracy of the base system. In the article, we present our approach to multi-modal (audio-video) emotion recognition system. For audio sub-system, a feature set comprised of prosodic, spectral and cepstrum features is selected and support vector classifier is used to produce the scores for each emotional category. For video sub-system a novel approach is presented, which does not rely on the tracking of specific facial landmarks and thus, eliminates the problems usually caused, if the tracking algorithm fails at detecting the correct area. The system is evaluated on the eNTERFACE database and the recognition accuracy of our audio-video fusion is compared to the published results in the literature.
Rok Gajsek, Vitomir Struc, France Mihelic
Added 23 Jun 2010
Updated 23 Jun 2010
Type Conference
Year 2010
Where ICPR
Authors Rok Gajsek, Vitomir Struc, France Mihelic
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