THU Face Database for Real-Time Automatic Video Scoring Model

With the rapid development of electronic techniques, the Internet video services have already covered billions of users and become the mainstream media and major marketing channel in recent years. Along with the increasing importance of video, peoples interest in video have becoming far more important and influential. Still, there is no real-time video evaluation system reflect people’s interest Meter. This paper aims at build a facial video database providing standardized test video sequences, which is also an open database for any research related to facial expression and interest meter. Furthermore, Backpropagation (BP) neural network and Self organizing Feature Map (SOFM) neural network are used to establish the video scoring model. Our database contains over 1000 videos from 82 subjects, each frame of which is processed into a recognized facial expression. Simulation results show that the accuracy of our automatic video method can reach 85.6% based on our facial video database. Free examples can be downloaded at http://www.facedbv2.com.

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