Abstract
Most face recognition and for monitoring and human – human-computer interaction (HCI) technology tracking system relies on the assumption that in the face of the positive view. Alternative method, the image of face angle to the direction of knowledge can improve performance based on non-frontal view technology. Human face location detection in the city plays an important role in the continuous application of the surveillance video, such as face recognition, face recognition, face a snapshot image screening to save storage capacity. In this chapter, we propose a method for human face location based on Haar features and LVQ technology. First, we performed the eye location based on Haar features. Then, we face image into binary image a number of maps and statistical information on the position of the eyes. After obtaining the statistical distribution of pixels, we based on LVQ neural network classifier classifies the face direction. Based on the results, our algorithm can detect up to 95%. Through the implementation of face direction, we can get the best upright frontal face image recognition and most distinctive quality for further application.
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Xu, Z., Liu, Y., Mei, L., Hu, C., Chen, L.: Semantic based representing and organizing surveillance big data using video structural description technology. J. Syst. Softw. 102, 217–225 (2015)
Hu, C., Xu, Z., Liu, Y., Mei, L., Chen, L., Luo, X.: Semantic link network-based model for organizing multimedia big data. IEEE Trans. Emerg Top Comput. 2(3), 376–387 (2014)
Wu, L., Wang, Y.: The process of criminal investigation based on grey hazy set. In: Proceedings of 2010 IEEE International Conference on System Man and Cybernetics, pp. 26–28 (2010)
Liu, L., Li, Z., Delp, E.: Efficient and low-complexity surveillance video compression using backward-channel aware Wyner-Ziv video coding. IEEE Trans. Circuits Syst. Video Technol. 19(4), 452–465 (2009)
Zhang, J., Zulkernine, M., Haque, A.: Random-forests-based network intrusion detection systems. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 38(5), 649–659 (2008)
Yu, H., Pedrinaci, C., Dietze, S., Domingue, J.: Using linked data to annotate and search educational video resources for supporting distance learning. IEEE Trans. Learn. Technol. 5(2), 130–142 (2012)
Xu, C., Zhang, Y., Zhu, G., Rui, Y., Lu, H., Huang, Q.: Using webcast text for semantic event detection in broadcast sports video. IEEE Trans. Multimed. 10(7), 1342–1355 (2008)
Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284(5), 34–43 (2001)
Ma, H., Zhu, J., Lyu, M., King, I.: Bridging the semantic gap between image contents and tags. IEEE Trans. Multimed. 12(5), 462–473 (2010)
Chen, H., Ahuja, N.: Exploiting nonlocal spatiotemporal structure for video segmentation. In: Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 741–748 (2012)
Acknowledgment
The authors of this paper are members of Shanghai Engineering Research Center of Intelligent Video Surveillance. This work was supported in part the National Natural Science Foundation of China under Grant 61300202, 61332018, 61403084. Our research was sponsored by Program of Science and Technology Commission of Shanghai Municipality (No. 15530701300, 15XD15202000, 16511101700), in part by the technical research program of Chinese ministry of public security (2015JSYJB26).
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Yan, Z., Du, H., Xu, Z. (2018). Face Detection and Description Based on Video Structural Description Technologies. In: Abawajy, J., Choo, KK., Islam, R. (eds) International Conference on Applications and Techniques in Cyber Security and Intelligence. ATCI 2017. Advances in Intelligent Systems and Computing, vol 580. Edizioni della Normale, Cham. https://doi.org/10.1007/978-3-319-67071-3_2
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DOI: https://doi.org/10.1007/978-3-319-67071-3_2
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