Abstract
This paper proposes a robust and efficient eye state detection method based on an improved algorithm called LBP+SVM mode. LBP (local binary pattern) methodology is first used to select the two groups of candidates from a whole face image. Then corresponding SVMs (supporting vector machine) are employed to verify the real eye and its state. The LBP methodology makes it robust against rotation, illumination and occlusion to find the candidates, and the SVM helps to make the final verification correct.
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References
Dinges, D.F., Grace, R.: PERCLOS: a valid psychophysiological measure of alertness as assesed by psychomotor vigilance, Indianapolis. In: Federal Highway Administration, Office of Motor Carriers, Tech. Rep. MCRT-98-006 (1998)
Liu, Z., Ai, H.: Automatic Eye State Recognition and Closed-eye Photo Correction. In: 19th International Conference on Pattern Recognition (ICPR 2008), Tampa, Florida, USA, December 8-11 (2008)
Kith, V., El-Sharkawy, M., Bergeson-Dana, T., El-Ramly, S., Elnoubi, S.: A feature and appearance based method for eye detection on gray intensity face images. In: International Conference on Computer Engineering & Systems, ICCES 2008, November 25-27, pp. 41–47 (2008)
Gan, L., Cui, B., Wang, W.: Driver Fatigue Detection Based on Eye Tracking. In: The Sixth World Congress on Intelligent Control and Automation, WCICA 2006, vol. 2, pp. 5341–5344 (2006)
Zheng, Y., Wang, Z.: Robust and precise eye detection based on locally selective projection. In: 19th International Conference on Pattern Recognition, ICPR 2008, December 8-11, pp. 1–4 (2008)
Ojala, T., PietikaÈinen, M., Harwood, D.: A Comparative Study of Texture Measures with Classification Based on Feature Distributions. Pattern Recognition 29, 51–59 (1996)
Ojala, T., PietikaÈinen, M., MaÈenpaÈa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Ahonen, T., Hadid, A., Pietikainen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)
Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D.: The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations. IEEE Trans. on System Man, and Cybernetics (Part A) 38(1), 149–161 (2008)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2009), http://www.csie.ntu.edu.tw/~cjlin/libsvm (updated, February 20)
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Sun, R., Ma, Z. (2009). Robust and Efficient Eye Location and Its State Detection. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_34
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DOI: https://doi.org/10.1007/978-3-642-04843-2_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04842-5
Online ISBN: 978-3-642-04843-2
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