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With the prevalence of video surveillance and the extraordinary number of online video resources, the demand for effective and efficient content-based video analysis tools has shown significant growth in recent years. Human face has always been one of the most important interest points in automatic video analysis. In this paper, we designed a face-based video retrieval system. We analyzed the three key issues in constructing such systems: frame extraction based on face detection, key frame selection based on face tracking and relevant video retrieval using PCA-based face matching. In order to cope with the huge number of videos, we implemented a prototype system on the Hadoop distributed computing framework: DiFace. We populated the system with a baseline dataset consisting of TED talk fragments, provided by the 2014 Chinese national big data contest. Empirical experimental results showed the effectiveness of the system architecture and also the techniques employed.

DiFace: A Face-based Video Retrieval System with Distributed Computing