Most of advertisement systemsare 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.
References
[1]
J. H. Cho, Y. J. Sah and J. Ryu, “A New Content-related Advertising Model for Interactive Television,” Broad-band Multimedia Systems and Broadcasting 2008, March 31 2008-April 2 2008, pp. 1-9.
[2]
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.
[3]
J. Kim and S. Kang, “An Ontology-Based Personalized Target Advertisement System on Interactive TV,” Consumer Electronics (ICCE), 2011 IEEE International Conference, 9-12 Jan. 2011，pp. 895 - 896.
[4]
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.
[5]
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.
[6]
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
[7]
J. X. Ruan and J. X. Yin “Multi-Pose Face Detection Using Facial Features and AdaBoost Algorithm,” Second International Workshop on Computer Science and Engineering, 2009, pp. 31-34.
[8]
Y.-W. Wu and X.-Y. Ai, “Face Detection in Color Images Using AdaBoost Algorithm Based on Skin Color Information,” Workshop on Knowledge Discovery and Data Mining, 2008, pp. 339-342.
[9]
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
[10]
Y. C. Xing, Z. Z. Wang and W. P. Qiang, “Face Tracking Based Advertisement Effect Evaluation,” Image and Signal Processing, 2009. CISP '09. 2nd, 2009, pp. 1-4.
