Finding and Improving the Key-Frames of Long Video Sequences for Face Recognition

Face recognition systems are very sensitive to the quality and resolution of their input face images. This makes such systems unreliable when working with long surveillance video sequences without employing some selection and enhancement algorithms. On the other hand, processing all the frames of such video sequences by any enhancement or even face recognition algorithm is demanding. Thus, there is a need for a mechanism to summarize the input video sequence to a set of key-frames and then applying an enhancement algorithm to this subset. This paper presents a system doing exactly this. The system uses face quality assessment to select the key-frames and a hybrid super-resolution to enhance the face image quality. The suggested system that employs a linear associator face recognizer to evaluate the enhanced results has been tested on real surveillance video sequences and the experimental results show promising results.