Most of advertisement systems are presently still launch the publicity content by the static words and pictures. Recently, this static advertisement model will not be able to attract people’s attention more and more. Moreover, the static information content of advertisement system is limited because of the layout shown size. It can not also fully demonstrate the information content of advertisement system. In this paper, we develop a digital interactive kanban advertisement system using face recognition methodology to solve these problems. The system captures the person’s face through the camera. The digital advertisement content size is relevant by the person and camera observation locations. In this paper, we adopt the Adaboost algorithm to judge people face, and the system only need to grab the position of the face. The system doesn’t built expensive and complex equipment to reduce the system cost and enhance the system performance. This system can also achieve the same similar digital interactive advertising effectiveness.
 M.-H. Hsieh, D.-L. Yang and J.-Y. Dai, “A Face Recognition System Prototype to Evaluate the Effectiveness of Digital Advertisement,” 2010 Conference on Computer Vision, Image Processing and Information Technology, 2010-06. Zhongli, Taiwan, pp. 283-289.
 M. Taspinar, A. T. Naskali, M. Kurt and G. Eren, “The Importance of Customized Advertisement Delivery Using 3D Tracking and Facial Recognition,” in Proc. The Second International Conference on Digital Information and Communication Technology and its Applications (DICTAP), 2012, pp. 526-530.
 S. Guerfi, J.-P. Gambotto and S. Lelandais, “Implementation of the Watershed Method in the HSI Color Space for the Face Extraction,” Advanced Video and Signal Based Surveillance, Sept. 2005, pp. 282-286.
 M. Rahman and N. Kehtarnavaz, “Real-TimeFace-Priority Auto Focus for Digital and Cell-Phone Cameras,” IEEE Transactions on Consumer Electronics, Vol. 54, No. 4, 2008, pp. 1506-1513.doi:10.1109/TCE.2008.4711194
 S. A. Inalou and S. Kasaei “AdaBoost-Based Face Detection in Color Images with Low False Alarm,” Second International Conference on Computer Modeling and Simulation, 2010, pp.101-111. doi:10.1109/ICCMS.2010.287