Real-Time Face Detection Using a Moving Camera

This paper presents a real-time face detection system using a moving camera. The proposed system consists of three modules, including (1) detection of face candidates: Face candidates are generated using the information of skin color, edges, and face area, (2) verification of face candidates: HOG (Histogram of Oriented Gradient) features are generated from face candidates and a two-class C-SVM (Support Vector Machine) classifier with pretrained face samples is employed to determine whether face candidates are real faces or not, (3) face tracking: Overlapping area of two face targets in current and previous frames is estimated to determine whether the tracking will be continuous or not. By use of estimation of face size, the proposed method can avoid a huge amount of computation time that is required by a point-by-point scanning way in conventional methods. Moreover, the accuracy of the face detection can be improved greatly. The proposed system can successfully detect most faces of the crowds in open space, which is beneficial for quickly searching the specified persons to prevent the occurrence of possible criminal events.