Boosting Face in Video Recognition via CNN Based Key Frame Extraction

Abstract

Face in video recognition (FiVR) technology is widely applied in various fields such as video analytics and real-time video surveillance. However, FiVR technology also faces the challenges of high-volume video data, real-time processing requirement, as well as improving the performance of face recognition (FR) algorithms. To overcome these challenges, frame selection becomes a necessary and beneficial step before the FR stage. In this paper, we propose a CNN-based key-frame extraction (KFE) engine with GPU acceleration, employing our innovative Face Quality Assessment (FQA) module. For theoretical performance analysis of the KFE engine, we evaluated representative one-person video datasets such as PaSC, FiA and ChokePoint using ROC and DET curves. For performance analysis under practical scenario, we evaluated multi-person videos using ChokePoint dataset as well as in-house captured full-HD videos. The experimental results show that our KFE engine can dramatically reduce the data volume while improving the FR performance. In addition, our KFE engine can achieve higher than real-time performance with GPU acceleration in dealing with HD videos in real application scenarios.