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http://dx.doi.org/10.9717/JMIS.2018.5.1.15

Triangle Method for Fast Face Detection on the Wild  

Malikovich, Karimov Madjit (Government testing center of the Republic of Uzbekistan)
Akhmatovich, Tashev Komil (Dean of the Computer engineering faculty of Tashkent university of information technologies named after Muhammad al-Khwarizmi)
ugli, Islomov Shahboz Zokir (Tashkent university of information technologies named after Muhammad al-Khwarizmi)
Nizomovich, Mavlonov Obid (Samarkand branch of Tashkent university of information technologies named after Muhammad al-Khwarizmi)
Publication Information
Journal of Multimedia Information System / v.5, no.1, 2018 , pp. 15-20 More about this Journal
Abstract
There are a lot of problems in the face detection area. One of them is detecting faces by facial features and reducing number of the false negatives and positions. This paper is directed to solve this problem by the proposed triangle method. Also, this paper explans cascades, Haar-like features, AdaBoost, HOG. We propose a scheme using 12-net, 24-net, 48-net to scan images and improve efficiency. Using triangle method for frontal pose, B and B1 methods for other poses in neural networks are proposed.
Keywords
FPS; Facial; Haar-Like; HOG; AdaBoost; Cascade; Convolutional; Landmark; Capsular; Triangle Method; Pose; Feature; Neural Network;
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  • Reference
1 M. H. Yang, D. J. Kriegman, and N. Ahuja, "Detecting face in images: a survey," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 24, pp. 34-58, 2002.   DOI
2 Chao W. L, "Face Recognition," GICE, National Taiwan University, 2007.
3 P. Viola and M. J. Jones, "Robust real-time face detection," International journal of computer vision, Vol. 57, No. 2, pp. 137-154, 2004.   DOI
4 Malikovich, K. M., Axmatovich, T. K., Zokirugli, I. S., and Zarif, K. "Minimizing in Face Recognition Errors and Preprocessing Time," In Proceedings of International Conference on Application of Information and Communication Technology and Statistics in Economy and Education (ICAICTSEE), pp. 212, 2014.
5 Dalal N., Triggs B, "Histograms of oriented gradients for human detection," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, pp.886-893, 2005.
6 H. Li, Z. Lin, X. Shen, J. Brandt, and G. Hua, "A convolutional neural network cascade for face detection," in IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325-5334, 2015.
7 C. Zhang, and Z. Zhang, "Improving multiview face detection with multi-task deep convolutional neural networks," IEEE Winter Conference on Applications of Computer Vision, pp. 1036-1041, 2014.
8 Sabour S., Frosst N., Hinton G. E. "Dynamic routing between capsules," Advances in Neural Information Processing Systems, pp. 3859-3869, 2017.
9 Zhang K et al. "Detecting Faces Using Inside Cascaded Contextual CNN," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3171-3179, 2017.
10 Karimov M. M. Islomov Sh. Z. "Optimising And Recommendations For Collecting Face Databases," International Journal of Research in Engineering and Science. pp. 2320-9364.
11 H. Li, Z. Lin, X. Shen, J. Brandt, G. Hua, "A convolutional neural network cascade for face detection," in IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325-5334, 2015.
12 Crow F. C, "Summed-area tables for texture mapping," ACM SIGGRAPH computer graphics, No.3 pp. 207-212, ACM, 1984.