Face and head detection for a real-time surveillance system

Abstract

This paper describes a face and head detection method for a real-time surveillance system. Since there is no guarantee that surveillance cameras can capture frontal face or full-body of human, face and head detection has an advantage for the practical use. Proposed method employs four directional features and linear discriminant analysis. It can detect face and head simultaneously, and reduce computation cost. In the experiments, comparison of two classifiers and evaluation of proposed human detection method were performed using still images and video scenes. The results showed that the performances of two classifiers were almost equivalent. Thus, the classifier labeled face samples to one class was better in terms of computation cost. In the human detection experiment, the results were 87.2% (48/55) for human detection rate, and 83.6% (832/995) for reliability of detection. The proposed detection method was implemented on a PC and run at approximately over 10 fps for VGA input with motion detection.