A multi-view camera-based anti-fraud system and its applications

Anti-fraud system is very useful in many intelligent applications. With the development of the financial field, anti-fraud system is becoming more and more important. But conventional face recognition techniques cannot distinguish real faces and masks effectively from the video stream. In addition, computing the whole video stream is redundant and time-consuming. So computing some key frames selected from video stream is more effective, but many frames gotten randomly from video stream don't contain any faces. So in this paper, we propose a new method only with a camera to estimate a person's behavior to detect whether a person is fraudulent. A camera-based anti-fraud system requires a series of representative video frames (i.e. key frames) from the video stream. First, a set of key frames are extracted from the video stream using our active key frame selection algorithm. The criterion is that the contents of the video stream are maximally covered by these frames. Then, face features are obtained using Dlib. Finally, a probabilistic model is proposed to estimate a person's behavior. Experimental results have demonstrated that: 1) our key frame selection algorithm can reduce redundant frames effectively; 2) our system can estimate a person's behavior in real time.