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http://dx.doi.org/10.15207/JKCS.2019.10.6.007

Improved Skin Color Extraction Based on Flood Fill for Face Detection  

Lee, Dong Woo (Dept of Plasma Bio Display, KwangWoon University)
Lee, Sang Hun (Ingenium College of Liberal Arts, KwangWoon University)
Han, Hyun Ho (Institute of Information Technology, KwangWoon University)
Chae, Gyoo Soo (Division of Information Communication Eng., Baekseok University)
Publication Information
Journal of the Korea Convergence Society / v.10, no.6, 2019 , pp. 7-14 More about this Journal
Abstract
In this paper, we propose a Cascade Classifier face detection method using the Haar-like feature, which is complemented by the Flood Fill algorithm for lossy areas due to illumination and shadow in YCbCr color space extraction. The Cascade Classifier using Haar-like features can generate noise and loss regions due to lighting, shadow, etc. because skin color extraction using existing YCbCr color space in image only uses threshold value. In order to solve this problem, noise is removed by erosion and expansion calculation, and the loss region is estimated by using the Flood Fill algorithm to estimate the loss region. A threshold value of the YCbCr color space was further allowed for the estimated area. For the remaining loss area, the color was filled in as the average value of the additional allowed areas among the areas estimated above. We extracted faces using Haar-like Cascade Classifier. The accuracy of the proposed method is improved by about 4% and the detection rate of the proposed method is improved by about 2% than that of the Haar-like Cascade Classifier by using only the YCbCr color space.
Keywords
Haar-like; Cascade Classifier; Skin Extraction; Flood Fill; YCbCr Color Space;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 H. J. Kim, Y. S. Park, K. B. Kim & S. H. Lee. (2019). Modified HOG Feature Extraction for Pedestrian Tracking. Journal of the Korea Convergence Society, 10(3), 39-47.   DOI
2 S. K. Pyo, Y. S. Park, G. S. Lee & S. H. Lee. (2019). Hangeul detection method based on histogram and character structure in natural image. Journal of the Korea Convergence Society, 10(3), 15-22.   DOI
3 T. H. Yoo, G. S. Lee & S. H. Lee. (2012). Window Production Method based on Low-Frequency Detection for Automatic Object Extraction of GrabCut. Journal of Digital Convergence, 10(8), 211-217   DOI
4 H. H. Han, G. S. Lee, J. Y. Lee & S. H. Lee. (2012). Region Segmentation Technique Based on Active Contour for Object Segmentation, Journal of Digital Convergence, 10(3), 167-172.   DOI
5 J. H. Park. (2016). Improved Face Detection Algorithm Using Color Distribution and Shape Characteristics, Graduate dissertation. Kwangwoon University, Seoul
6 J. S Oh (2018). Improved Face Detection Algorithm Using Face Verification. Journal of the Korea Institute of Information and Communication Engineering, 22(10), 1334-1339   DOI
7 M. A. Turk & A. P. Pentland. (1991). Face recognition using eigenfaces. In Proceedings, 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 586-591). IEEE.
8 S. F, Hafez, M. M. Selim & H. H. Zayed. (2015). 2d face recognition system based on selected gabor filters and linear discriminant analysis lda. Conference on Computer Vision and Pattern Recognition. Boston: IEEE
9 S. Chang & S. Yoo. (2000). Face Detection Using Color Information,. The Journal of Korean Institute of Communications and Information Sciences, 25(6), 1012-1020.
10 R. L. Hsu, M. Abdel-Mottaleb & A. K. Jain. (2002). Face detection in color images. IEEE transactions on pattern analysis and machine intelligence, 24(4), 696-706   DOI
11 W. Chen, T. Sun, X. Yang & L. Wang. (2009, August). Face detection based on half face-template. In 2009 9th International Conference on Electronic Measurement & Instruments. (pp. 4-54). Beijing : IEEE.
12 D. N. Chandrappa, M. Ravishankar & D. R. RameshBabu. (2011, April). Face detection in color images using skin color model algorithm based on skin color information. In 2011 3rd International Conference on Electronics Computer Technology. (1, pp. 1988-2018) Kanyakumari : IEEE.
13 C. M. Kim & K. W. Lee. (2014). Face Region Detection of a Pedestrian Using Ω-Feature in Video Surveillance System, Journal of KIIT. 14(9), 27-36
14 C. A. Perez, V. A. Lazcano & P. A. Estevez. (2007). Real-time iris detection on coronal-axis-rotated faces. IEEE Transactions on Systems, Man, and Cybernetics, Part C(Applications and Reviews), 37(5), 971-978.   DOI
15 I. Kallenbach, R. Schweiger, G. Palm & O. Lohlein. (2006, September). Multi-class object detection in vision systems using a hierarchy of cascaded classifiers. In Intelligent Vehicles Symposium, (pp. 383-387). Tokyo : IEEE.
16 W. Wang & H. Niu. (2012). Face detection based on improved AdaBoost algorithm in E-Learning. In Cloud Computing and Intelligent Systems.2012 IEEE 2nd International Conference on (pp. 924-927). Hangzhou : IEEE.
17 D. C. Kim, C. H. Lee, M. H. Choi & Y. H. Haa. (2012). Skin Detection Method using Color Space based Methods and Focus Region. Journal of Korean Society for Imaging Science and Technology, 18(4), 16-22.
18 P. Viola & M. J. Lee.(2004). Robust real-time face detection, Proceedings Eighth IEEE International Conference on Computer Vision. (pp. 137-154) Vancouver : IEEE
19 E. M. Nosal. (2008, October). Flood-fill algorithms used for passive acoustic detection and tracking. In New Trends for Environmental Monitoring Using Passive Systems, (pp. 1-5). Hyeres : IEEE.