DETERMINATION OF EFFICIENCY OF COLOR CHARACTERISTICS IN FRAME OF VIOLA–JONES ALGORITHM BASED ON PERSONS LOCATION
Keywords:
Viola–Jones algorithm, localization of image, skin density ratio, optimization of the geometric parameter cognition windowAbstract
The paper showed color characteristics of human skin effecting localization problems solved by Viola-Jones algorithm in scope of face image detecting.
There has been offered the approach to optimizing the geometrical parameters of the results of localization using an additional parameter – the skin density coefficient. Based on the training set (13225 input images) using iterative procedure there has been determined the optimum value of this coefficient and considered the average effectiveness of the process to reduce geometric parameters for a localized image. Which is significantly affects the size of the stored results and the results of further recognition.
References
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2. Zhenchao Xu. Improving Detector of Viola and Jones through SVM / Zhenchao Xu, Li Song, Jia Wang, Yi Xu, // Computer Vision — ACCV 2010 Workshops, ACCV 2010 International Workshops — Queenstown, New Zealand — November 8—9, 2010. — P. 64—73.
3. Alpika Gupta. Face Detection Using Modified Viola Jones Algorithm / Alpika Gupta, Dr. Rajdev Tiwari // International Journal of Recent Research in Mathematics Computer Science and Information Technology — October 2014 — March 2015. — Vol. 1. — P. 59—66.
4. Shanshan Wang. Improved Viola-Jones Face Detector / Shanshan Wang, Amr Abdel-Dayem,// Proceedings of Taibah University International Conference on Computing and Information Technology — 2012 — P. 123—128.
5. Лисак Н. В. Підвищення якості розпізнавання методом Віоли–Джонса в задачах інформаційної безпеки підприємства шляхом попередньої обробки зображень / Н. В. Лисак, Ю. В. Міронова, І. О. Марченко, С. О. Петров // Оптико-електронні інформаційно-енергетичні технології — 2015 — № 1. — С. 70—75.
6. Neural network approach for image chromatic adaptation for skin color detection / [N. Bourbakis, P. Kakumanu, S. Makrogiannis, R. Bryll, and S. Panchanathan] // Int J NeuralSyst. — Feb. 2007. — Vol. 17. — P. 1—12.
7. D. Chai, A bayesian skinnon-skin color classifier usingn on parametric density estimation / D. Chai, S. L. Phung, and A. Bouzerdoum. // IEEE Int. Symposium on Circuits and Systems 2003. — Bangkok, Thailand. — 2003. — P. 464—467.
8. Maximum entropy models for skin detection. / [B. Jedynak, H. Zheng, M. Daoudi, and D. Barret] // Universite des Scienceet Technology de Lille, France, Technicalreport. — 2002. — P. 276—281.
9. K. S. Ravichandran. Color skin segmentation using k-meanscluster / K. S. Ravichandran, B. Ananthi // International Journal of Computational and Applied Mathematics. — 2009. — vol. 4 — P. 153—157.
10. The effect of age on skin color and color heterogeneityin fourethnic groups / DeRigal J, Des Mazis I, Diridollou S, Querleux B, Yang G, Leroy F, Barbosa VH. // L'Oréal Recherche, Chevilly, France. Skin Research and Technology — 05. 2010 — P. 168—178.
11. Vezhnevets V. A Surveyon Pixel-Based Skin Color Detection Techniques / Vladimir Vezhnevets, Vassili Sazonov, Alla Andreeva. // IN PROC. GRAPHICON. — 2003. — P. 85—92.
12. Face Recognition by Elastic Bunch Graph Matching», In Intelligent Biometric Techniquesin Fingerprint and Face Recognition / [Laurenz Wiskott, Jean-Marc Fellous, Norbert Kuger, Christoph vonder Malsburg]. — 1999 — P. 355—396.
13. Edwards G. J. Face Recognition Using Active Appearance Models / G. J. Edwards, T. F. Cootes, and C. J. Taylor // Computer Vision — ECCV’98, of the series Lecture Notesin Computer Science. — P. 581—595.
14. Labeled Facesin the Wild Home [Електронний ресурс]. — Режим доступу : http://vis-www.cs.umass.edu/lfw/.
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Published
2016-03-16
How to Cite
[1]
I. O. Marchenko, S. O. Petrov, and N. V. Lysak, “DETERMINATION OF EFFICIENCY OF COLOR CHARACTERISTICS IN FRAME OF VIOLA–JONES ALGORITHM BASED ON PERSONS LOCATION”, Вісник ВПІ, no. 1, pp. 108–114, Mar. 2016.
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Information technologies and computer sciences
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