Abstract
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. Most of conventional facial expression techniques adopt supervised learning methods. This is a crucial problem because preparing sufficient training data requires considerable human effort due to the diversity of facial expressions observed in lifelog videos. We thus propose a more efficient emotional scene detection method using an unsupervised facial expression recognition on the basis of cluster ensembles. Our approach does not require any training data sets and is able to detect various emotional scenes. The detection performance of the proposed method is evaluated through an emotional scene detection experiment.
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Nomiya, H., Morikuni, A., Hochin, T. (2015). An Unsupervised Ensemble Approach for Emotional Scene Detection from Lifelog Videos. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Studies in Computational Intelligence, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-319-10389-1_11
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DOI: https://doi.org/10.1007/978-3-319-10389-1_11
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