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http://dx.doi.org/10.5370/KIEE.2015.64.1.082

A Technique of Feature Vector Generation for Eye Region Using Embedded Information of Various Color Spaces  

Park, Jung-Hwan (Dept. of Computer Engineering, Daejin Univerity)
Shin, Pan-Seop (Dept. of Computer Engineering, Daejin Univerity)
Kim, Guk-Boh (Dept. of Computer Engineering, Daejin Univerity)
Jung, Jong-Jin (Dept. of Computer Engineering, Daejin Univerity)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.64, no.1, 2015 , pp. 82-89 More about this Journal
Abstract
The researches of image recognition have been processed traditionally. Especially, face recognition technology has been received attractions with advance and applied to various areas according as camera sensor embedded into many devices such as smart phone. In this study, we design and develop a feature vector generation technique of face for making animation caricatures using methods for face detection which are previous stage of face recognition. At first, we detect both face region and detailed eye region of component element by Viola&Johns's realtime detection method which are called as ROI(Region Of Interest). And then, we generate feature vectors of eye region by utilizing factors as opposed to the periphery and by using appearance information of eye. At this point, we focus on the embedded information in many color spaces to overcome the problems which can be occurred by using one color space. We propose a feature vector generation method using information from many color spaces. Finally, we experiment the test of feature vector generation by the proposed method with enough quantity of sample picture data and evaluate the proposed method for factors of estimating performance such as error rate, accuracy and generation time.
Keywords
Face recognition; Face detection; ROI; Feature vector; Color space;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 J. R. Parker, “Algorithm for Image Processing and Computer Vision 2nd edition, Wiley Publishing, pp. 285-319, 2011
2 T. P. Weldon, W. E. Higgins, “Design of multiple Gabor filters for texture segmentation”, Acoustics, Speech, and Signal Processing, Conference Proceedings.,IEEE International Conference on. Vol. 4, pp. 2243-2246, May 1996.
3 Y. Freund and R. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting”, Journal of Computer and System Sciences, Vol. 55, No. 1, pp. 119-139, Aug. 1997.   DOI   ScienceOn
4 Z. Zheng, J. Yang, L. Yang, “A robust method for eye features extraction on color image”, Pattern Recognition Letters, Vol. 26, Issue 14, pp. 2252-2261. Oct. 2005.   DOI   ScienceOn
5 H. S. Yum, M. Hong, Y. J. Choi, “Design and Implementation of Eye-Gaze Estimation Algorithm based on Extraction of Eye Contour and Pupil Region”, The Journal of Korean association of computer education, Vol. 17, No. 2, pp. 107-113, March 2014.
6 J. J. Jung, J. H. Park, M. Y. Jung, J. H. Kim, H. S. Suh, “A Design of Incremental Feature Vectors Extraction of Lip Shape Using Morphological Information”, International Conference on Convergence Technology, Vol. 4 No. 1, pp. 47-48, July 2014.
7 M. Montazeri, H. Nezamabadi-pour, “Automatic extraction of eye field from a gray intensity image using intensity filtering and hybrid projection function”, Communications, Computing and Control Applications, IEEE International Conference on. pp. 1-5, March 2011.
8 N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 9, Issue 1, pp. 62-66, 1979.   DOI   ScienceOn
9 OpenCV, “http://www.opencv.org/”
10 P. VIOLA and M. J. JONES, “Robust Real-Time Face Detection,” International Journal of Computer Vision, Vol. 57, Issue 2, pp. 137-154, May 2004.   DOI
11 S. chitra, G. Balakrishnan, Comparative Study for Two Color Spaces HSCbCr and YCbCr in Skin Color Detection, Applied Mathematical Sciences, Vol. 6, No. 85, pp. 4229-4238. 2012.
12 S. Y. Gwon, C. W. Cho, W. O. Lee, H. C. Lee, K. R. Park, H. K. Lee, J. J. Cha, “Comparative Performance Evaluations of Eye Detection algorithm”, Journal of Korea Multimedia Society, Vol 15, No 6, pp. 722-730, June 2004.   DOI
13 B. S. Kim, H. Lee, W. Y. Kim, “ Rapid eye detection method for non-glasses type 3D display on portable devices”, Consumer Electronics, IEEE Transactions on, Vol. 56, Issue 4, pp. 2498-2505, Nov. 2010.   DOI   ScienceOn
14 C. Dorin. V. Ramesh, P. Meer, “Real-time tracking of non-rigid objects using mean shift”, Computer Vision and Pattern Recognition, Proceedings. IEEE Conference on, Vol. 2, pp. 142-149, June 2000.
15 R. P. Gaur, K. N. Jariwala, “A Survey on Methods and Models of Eye Tracking, Head Pose and Gaze Estimation”, Journal of Emergeing Technologies and Innovative Research, Vol. 1, Issue 5, pp. 265-273, Oct. 2014.
16 H. S. Koo, H. G. Song, “A Study on the Eye-line Detection from Facial Image taken by Smart Phone”, Journal of Korea Institute of Information and Communication Engineering, Vol. 15, No. 10, Oct. 2011.