Summarization of Surveillance Video Sequences Using Face Quality Assessment

Constant working surveillance cameras in public places, such as airports and banks, produce huge amount of video data. Faces in such videos can be extracted in real time. However, most of these detected faces are either redundant or useless. Redundant information adds computational costs to facial analysis systems and useless data makes the final results of such systems noisy, unstable, and erroneous. Thus, there is a need for a mechanism to summarize the original video sequence to a set of the most expressive images of the sequence. The proposed system in this paper uses a face quality assessment technique for this purpose. The summarized results of this technique have been used in three different facial analysis systems and the experimental results on real video sequences are promising.