Face Detection and Tracking System with Block-Matching, Meanshift and Camshift Algorithms and Kalman Filter

The aims of this paper is to propose a novel method for solving problem face detection and tracking system based in four algorithms: block-matching (BMA), Meanshift, Camshift and Kalman filter. Firstly, BMA is applied to the different sequential test as a preprocessing stage to detect faces. Then, the face tracking system are with three modules: Meanshift, Camshift and Kalman filter. This scheme gives a better face detection and tracking. Our work increases the performance and other criteria values in the embedded system. The human is more and more interested in producing intelligence which is one of the most impressive features of natures. Researchers are trying to make intelligent machine that have various capabilities. Building a machine or robot is probably one of the most challenging problems which humans are trying to solve. Recently, many projects have started with the purpose of learning machines to track some particular objects. One of the most challenging applications in computer vision is tracking objects efficiently in video sequence. Though progress has been accomplished, the best algorithms are far from reaching the speed and the performance of system. Object or multi-object tracking (face, human, car, etc.) is a fundamental problem that merits particular attention, since it is the key to solve a number of computer vision applications.