Face Information Structuring Based on Data Mining from Large-Scale Broadcast Video Archive

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In this project, we achieved a higher-level facial recognition and structuration technique based on the integration of visual information and broadcasting metadata. The technique was applied to a large-scale broadcast video archive with broadcasting metadata of great variety. While proposing a facial image matching algorithm robust against the variation in illumination condition, pose, and expression, we formulated the classic facial recognition task into a quantitative prediction problem that estimates the event probability of cause-effect relationship accompanied by network path. The solution was achieved in the form of a novel facial classification approach based on a Bayesian network constructed on top of broadcasting metadata, which leverages the correlation between the broadcasting metadata and the broadcast video in a both effective and efficient manner.