REDUСING THE DIMENSION OF THE LBP-FEATURE SPACE IN A PROBLEM OF DETERMINING PERSONALITY ATTRIBUTES BY FACIAL IMAGE
Keywords:
computer vision, machine learning, image classification, personality attributes, local binary patterns, features extractionAbstract
We propose an approach to reduce the dimension of describing the facial image feature space formed by the method of local binary patterns (LBP) through the use of a priori information about the face and Adaboost algorithm for selecting the most significant features. The results of computational experiments showing, that the proposed approach reduces the image classification time almost in 8 times.
References
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3. Qiu X., Sun Zh., Tan T. Global Texture Analysis of Iris Images for Ethnic Classification // Lecture Notes in Computer Sci. 2005. V. 3832. P. 411 — 418.
4. Fu Y., Xu Y., Huang T.S. Estimating human ages by manifold analisys of face pictures and regression on aging features // Proc. IEEE Conf. multimedia and Expo. 2007. P. 1383 — 1386
5. Ramanathan N., Chellappa R. Modeling age progression in yang faces // Proc. IEEE Conf. computer vision and pattern recognition (CVPR’06). 2006. P. 387 — 394.
6. Хуанг Т.С. и др. Быстрые алгоритмы в цифровой обработке изображений. М.: Радио и связь, 1984.
7. Yang Z., Li M., Ai H. An experimental study on automatic face gender classification // Proc. Intern. Conf. Pattern Recognition (ICPR). 2006. P. 1099 — 1102.
8. Shan C. Learning local binary patterns for gender classification on real-world face images // Pattern Recognition Lett. 2012. V. 33 (4). P. 431 — 437.
9. Hadid A., Pietikainen M. Combining appearance and motion for face and gender recognition from videos// Pattern Recognition Lett. 2009. V. 42 (11). P. 2818 — 2827.
10. Maenpaa T. The Local binary pattern approach to texture analysis — extensions and applications. Oulu University Press, 2003.
11. Yilionias J., Hadid A., Hong X., Pietikainen M. Age estimation using locale binary patterns kernel density estimate // Proc. Intern. Conf. image analysis and processing (ICIAP’13). 2013. P. 141 — 150.
12. Lian H., Lu B. Multi-view gender classification using local binary patterns and support vector machines // Proc. Intern. Symp. on neural networks. 2006. P. 202 — 209.
13. Alexandre L.A. Gender recognition: a multiscale decision fusion approach // Pattern recognition Lett. 2010.V. 31. P. 1422 — 1427.
14. Chen C., Ross A. Evaluation of gender classification methods on thermal and near-infrared face images // Proc. Intern. joint Conf. on biometrics (IJCB). 2011. pp. 367 — 374.
15. Makinen E., Raisamo R. An experimental comparison of gender classification methods // Pattern recognition Lett. 2008. V. 29. P. 1544 — 1556.
16. Bellustin N. et al. Instant Human Face Attributes Recognition System // Intern. J. Advanced Computer Sci. and Appl.: Special Issue on Artificial Intelligence. 2011.P. 112 — 120.
17. Gutta S., Huang J., Jonathon P., Wechsler H. Mixture of Experts for Classification of Gender, Ethnic Origin, and Pose of Human Faces // IEEE Trans. on neural networks. 2000. V. 11. N 4. P. 948 — 960.
18. Yang Z., Ai H. Demographic classification with local binary patterns // Proc. IEEE Intern. Conf. biometrics. 2007. P. 464 — 473.
19. Ojala T., Pietikainen M., Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns // IEEE Trans. on pattern analysis and machine intelligence. 2002. V. 24 (7). P. 971 — 987.
20. Рыбинцев А.В., Лукина Т.М., Конушин В.С., Конушин А.С. Возрастная классификация людей по изображению лица на основе метода ранжирования и локальных бинарных шаблонов // Системы и средства информатики. 2013. Т. 23. № 2. C. 62 — 73
21. Вьюгин В.В. Математические основы теории машинного обучения и прогнозирования. М.: МЦМНО, 2013.
22. MORPH (Craniofacial Longitudinal Morphological Face Database) [Электрон. ресурс]. http://www.faceaginggroup.com/morph/ (дата обращения 01.12.2015).
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Published
2018-11-30
Issue
Section
Informatics, computer engineering and control (05.13.00)
