Abstract
Recently, there are so many videos available for people to choose to watch. To solve this problem, we propose a tagging system for video content based on facial expression that can be used for recommendations based on video content. Viewer’s face captured by a camera is extracted by Elastic Bunch Graph Matching, and the facial expression is recognized by Support Vector Machines. The facial expression is classified into Neutral, Positive, Negative and Rejective. Recognition results are recorded as ”facial expression tags” in synchronization with video content. Experimental results achieved an averaged recall rate of 87.61%, and averaged precision rate of 88.03%.
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Miyahara, M., Aoki, M., Takiguchi, T., Ariki, Y. (2008). Tagging Video Contents with Positive/Negative Interest Based on User’s Facial Expression. In: Satoh, S., Nack, F., Etoh, M. (eds) Advances in Multimedia Modeling. MMM 2008. Lecture Notes in Computer Science, vol 4903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77409-9_20
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DOI: https://doi.org/10.1007/978-3-540-77409-9_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77407-5
Online ISBN: 978-3-540-77409-9
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