Abstract
In this paper we propose a method to verify the existence of eyeglasses in the frontal face images by support vector machine. The difficulty of such task comes from the unpredictable illumination and the complex composition of facial appearance and eyeglasses. The lighting uncertainty is eliminated by feature selection, where the orientation and anisotropic measure is chosen as the feature space. Due to the nonlinear composition of glasses to face and the small quantity of examples, support vector machine(SVM) is utilized to give a nonlinear decision surface. By carefully choosing kernel functions, an optimal classifier is achieved from training. The experiments illustrate that our model performs well in eyeglasses verification.
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References
C.J.C. Burges: A Tutorial on Support Vector Machines for Pattern Rrecognition. Data Mining and Knowledge Discovery, Vol. 2, No. 2, 1998, pp.121–167.
Z. Jing and R. Mariani: Glasses Detection and Extraction By Deformable Contour. ICPR‘2000 Barcelona. September 2000, pp.3–9.
T. Joachims: Text Categorization With Support Vector Machines: Learning with Many Relevant Features, in Proc. of 10th European Conference on Machine Learning, Springer Verlag, 1998.
T. Joachims: Making Large-scale SVM Learning Practical, in Advances in Kernel Methods-Support Vector Learning, MIT Press, 1998.
M. Kass and A. Witkin: Analyzing orientated pattern, Computer Vision, Graphics and Image Processing, Vol.37, 1987, pp.362–397.
X.G. Lv, J. Zhou and C.S. Zhang: A Novel Algorithm for Rotated Human Face Detection, Proc. of CVPR, 2000, pp. 760–765.
E. Osuna, R. Freund and F. Girosi: Training Support Vector machines: An Application to Face Detection, Proc. of CVPR, 1997, pp.130–136.
J. Platt: Fast Training of Support Vector Machines Using Sequential Minimal Optimization, in Advances in Kernel Methods-Support Vector Learning, MIT Press, 1998.
Y. Saito, Y. Kenmochi and K. Kotani: Estimation of Eyeglassless Facial Images Using Principal Component Analysis, Proc. of ICIP, 1999, pp.197–201.
V. Vapnik: Statistical Learning Theory, J. Wiley, New York, 1998.
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© 2001 Springer-Verlag Berlin Heidelberg
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Wu, C., Liu, C., Zhou, J. (2001). Eyeglasses verification by support vector machine. In: Shum, HY., Liao, M., Chang, SF. (eds) Advances in Multimedia Information Processing — PCM 2001. PCM 2001. Lecture Notes in Computer Science, vol 2195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45453-5_155
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DOI: https://doi.org/10.1007/3-540-45453-5_155
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