An Efficient and Effective Framework for Eye Region Detection and Eye State Recognition
DOI:
https://doi.org/10.14738/aivp.25.479Keywords:
ye Region Detection, Eye State Recognition, Local Binary Pattern, Support Vector Machine.Abstract
This paper proposes a framework to treat the eye information in a face image including the eye region detection and the eye state recognition. In order to make it possible to employ our approach to a real application in practice, our goal in this paper is to design not only a fast enough but also a high performance framework for the eye region detection and the eye state recognition. Our proposed framework mainly contains two parts: the first is to locate the eye regions in a face image, and the second is to recognize the states, either open or closed, of the eye regions. When a frame is captured from a video sequence, a face detection method is first performed to determine the positions of face regions. Next, face regions are converted into binary images and then we perform the horizontal and the vertical projections to locate the eye regions. Two visual features containing the intensity values of pixels and local binary pattern (LBP) are extracted from eye regions to classify the eye states with the support vector machine (SVM) approach. This paper also demonstrates a several experiments to present the efficiency and effectiveness of our proposed framework.
References
. Tobii, http://www.tobii.com/
. SMI, http://www.smivision.com/en.html
. Ojala, T., Pietikäinen, M., and Harwood, D., A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 1996. 19(3): p. 51-59.
. Cortes, C. and Vapnik, V., Support-vector networks. Machine Learning, 1995. 20(3): p. 273-297.
. Zhou Z.-H. and Geng X., Projection functions for eye detection. Pattern recognition, 2004. 37(5): p. 1049-1056.
. Valenti R. and Gevers T., Accurate eye center location and tracking using isophote curvature. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008. p. 1-8.
. Wu Y., Liu H., and Zha H., A new method of detecting human eyelids based on deformable templates. Proceedings of IEEE Conference on Systems, Man and Cybernetics, 2004. p. 604-609.
. OpenCV, http://opencv.org/
. Wu J. and Trivedi M. M., An eye localization, tracking and blink pattern recognition system: algorithm and evaluation. ACM Transactions on Multimedia Computing, Communications, and Applications, 2010. 6(2): p. 599-601.
. Tan H. and Zhang Y.-J., Detecting eye blink states by tracking iris and eyelids. Pattern Recognition Letters, 2006. 27(6): p. 667-675.
. Dehnavi M., Attarzadeh N., and Eshghi M., Real time eye state recognition. Proceedings of the 19th Iranian Conference on Electrical Engineering, 2011. p. 1-4.
. Ojala T., Pietikainen M., and Maenpaa T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. 2497): p. 971-987.
. Xu C., Zheng Y., and Wang Z., Efficient eye states detection in real-time for drowsy driving monitoring system. Proceedings of International Conference on Information and Automation, 2008. p. 170-174.
. Wu Y.-S., Lee T.-W., Wu Q.-Z., and Liu H.-S., An eye state recognition method for drowsiness detection. Proceedings of IEEE Vehicular Technology Conference, 2010. p. 1-5.
. Hsu R.-L., Abdel-Mottaleb M., and Jain A. K., Face detection in color images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. 24(5): p. 696-706.
. Liao S., Jain A. K., Li S. Z., Partial face recognition: alignment-free approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013. 35(5): p. 1193-1205.
. Viola P. and Jones M. J., Robust real-time face detection. International journal of computer vision, 2004. 57(2): p. 137-154.
. Freund, Y., Schapire, R., and Abe, N., A short introduction to boosting. Journal-Japanese Society For Artificial Intelligence, 1999. 14: p. 771-780.
. Otsu N., A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics, 1979. 9(1): p. 62-66.
. Królak A. and Strumiłło P., Eye-blink controlled human-computer interface for the disabled. Advances in Intelligent and Soft Computing, 2009. 60: p. 123-133.
. [LIBSVM] LIBSVM: http://www.csie.ntu.edu.tw/~cjlin/.
