Unsupervised Emotional Scene Detection from Lifelog Videos Using Cluster Ensembles

Unsupervised Emotional Scene Detection from Lifelog Videos Using Cluster Ensembles

Hiroki Nomiya, Atsushi Morikuni, Teruhisa Hochin
Copyright: © 2013 |Pages: 15
DOI: 10.4018/ijsi.2013100101
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An emotional scene detection method is proposed in order to retrieve impressive scenes from lifelog videos. The proposed method is based on facial expression recognition considering that a wide variety of facial expression could be observed in impressive scenes. Conventional facial expression techniques, which focus on discriminating typical facial expressions, will be inadequate for lifelog video retrieval because of the diversity of facial expressions. The authors thus propose a more flexible and efficient emotional scene detection method using an unsupervised facial expression recognition based on cluster ensembles. The authors' approach does not need to predefine facial expressions and is able to detect emotional scenes containing a wide variety of facial expressions. The detection performance of the proposed method is evaluated through some emotional scene detection experiments.
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The facial expression recognition plays the most important role in the emotional scene retrieval. In order to precisely and efficiently recognize facial expressions, various kinds of facial expression recognition techniques have been proposed. The performance of the facial expression recognition can be greatly influenced by the facial features. Currently, there are two major types of facial features, appearance features and geometric features (Tian et al., 2011).

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