An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance

Face detection for security cameras monitoring large and crowded areas is very important for public safety. However, it is much more difficult than traditional face detection tasks. One reason is, in large areas like squares, stations and stadiums, faces captured by cameras are usually at a low resolution and thus miss many facial details. In this paper, we improve popular cascade algorithms by proposing a novel multi-resolution framework that utilizes parallel convolutional neural network cascades for detecting faces in large scene. This framework utilizes the face and head-with-shoulder information together to deal with the large area surveillance images. Comparing with popular cascade algorithms, our method outperforms them by a large margin.