GPU-Accelerated Key Frame Analysis for Face Detection in Video

Since video monitoring cameras now are implemented widely, video analytics has drawn much attention as a new research area. Correspondingly, there is an emerging need to detect faces through a period of video or a great number of videos to make comparisons with the stored identities in the database for personal identification or other purposes. Thus, face detection in video has gained great attention. However, when there are lots of videos or when the video is very long, the workload for face detection becomes very huge. As a result, the detecting time is prolonged. Thus, there is a need to detect faces quickly with reduced computation time. In this paper, we propose to add key frame analysis into the face detection process and employ the state-of-the-art graphic processing unit (GPU) platform to improve the overall performance. Our experimental results show that speedup as high as 3 folds can be achieved in terms of frames per second processed when GPU platform is introduced, compared with high-performance general-purpose CPUs. This shows great potential in applying GPU towards this kind of applications.