A Fusion Approach for Efficient Human Skin Detection

Wei Ren Tan, Chee Seng Chan, Pratheepan Yogarajah, Joan Condell

Research output: Contribution to journalArticlepeer-review

117 Citations (Scopus)

Abstract

A reliable human skin detection method that is adaptable to different human skin colors and illumination conditions is essential for better human skin segmentation. Even though different human skin-color detection solutions have been successfully applied, they are prone to false skin detection and are not able to cope with the variety of human skin colors across different ethnic. Moreover, existing methods require high computational cost. In this paper, we propose a novel human skin detection approach that combines a smoothed 2-D histogram and Gaussian model, for automatic human skin detection in color image(s). In our approach, an eye detector is used to refine the skin model for a specific person. The proposed approach reduces computational costs as no training is required, and it improves the accuracy of skin detection despite wide variation in ethnicity and illumination.To the best of our knowledge, this is the first method to employ fusion strategy for this purpose. Qualitative and quantitative results on three standard public datasets and a comparison with state-of-the-art methods have shown the effectiveness and robustness of the proposed approach.
Original languageEnglish
Pages (from-to)138-147
JournalIEEE Transactions on Industrial Informatics
Volume8
Issue number1
DOIs
Publication statusPublished - 1 Feb 2012

Bibliographical note

Reference text: [1] P. Vadakkepat, P. Lim, L. De Silva, L. Jing, and L. L. Ling, “Multimodal
approach to human-face detection and tracking,” IEEE Trans.
Ind. Electron., vol. 55, no. 3, pp. 1385–1393, Mar. 2008.
[2] C. S. Chan, H. Liu, and D. J. Brown, “Recognition of human motion
from qualitative normalised templates,” J. Intell. Robot. Syst., vol. 48,
no. 1, pp. 79–95, 2007.
[3] N. Kubota and K. Nishida, “Perceptual control based on prediction for
natural communication of a partner robot,” IEEE Trans. Ind. Electron.,
vol. 54, no. 2, pp. 866–877, Apr. 2007.
[4] O. Linda and M. Manic, “Fuzzy force-feedback augmentation for
manual control of multi-robot system,” IEEE Trans. Ind. Electron.,
vol. 58, no. 8, pp. 3213–3220, Aug. 2010.
[5] C. S. Chan, H. Liu, and D. J. Brown, “Human arm-motion classification
using qualitative normalized templates,” Lecture Notes Artif. Intell.,
vol. 4251, no. Part I, pp. 639–646, 2006.
[6] G. Pratl, D. Dietrich, G. P. Hancke, and W. T. Penzhorn, “A new
model for autonomous, networked control systems,” IEEE Trans. Ind.
Informat., vol. 3, no. 1, pp. 21–32, Feb. 2007.
[7] K. Sobottka and I. Pitas, “A novel method for automatic face segmentation,
facial feature extraction and tracking,” Signal Process.: Image
Commun., vol. 12, no. 3, pp. 263–281, 1998.
[8] H. Bae, S. Kim, B.Wang, M. H. Lee, and F. Harashima, “Flame detection
for the steam boiler using neural networks and image information
in the ulsan steam power generation plant,” IEEE Trans. Ind. Electron.,
vol. 53, no. 1, pp. 338–348, Feb. 2005.
[9] Y. Wang and B. Yuan, “A novel approach for human face detection
from color images under complex background,” Pattern Recognit., vol.
34, no. 10, pp. 1983–1992, 2001.
[10] D. Brown, I. Craw, and J. Lewthwaite, “A SOM based approach to skin
detection with application in real time systems,” in Proc. Brit. Mach.
Vis. Conf., 2001, pp. 491–500.
[11] M.-J. Seow, D. Valaparla, and V. K. Asari, “Neural network based skin
color model for face detection,” in Proc. Appl. Image Pattern Recognit.
Workshop, 2003, pp. 141–145.
[12] S. L. Phung, D. Chai, and A. Bouzerdoum, “A universal and robust
human skin colour model using neural network,” in Proc. Int. Joint
Conf. Neural Netw., 2001, vol. 4, pp. 2844–2849.
[13] N. Sebe, I. Cohen, T. S. Huang, and T. Gevers, “Skin detection: A
Bayesian network approach,” in Proc. Int. Conf. Pattern Recognit.,
2004, pp. 903–906.
[14] N. Friedman, D. Geiger, and M. Goldszmidt, “Bayesian network classifiers,”
Mach. Learn., vol. 29, pp. 131–163, Nov. 1997.
[15] M. J. Jones and J. M. Rehg, “Statistical color models with application
to skin detection,” Int. J. Comput. Vision, vol. 46, no. 1, pp. 81–96,
2002.
[16] R. Khan, A. Hanbury, and J. Stoettinger, “Skin detection: A random
forest approach,” in Proc. Int. Conf. Image Process., Hong Kong, 2010,
pp. 4613–4616.
[17] U. Yang, B. Kim, and K. Sohn, “Illumination invariant skin color segmentation,”
in Proc. 4th IEEE Int. Conf. Ind. Electron. Appl., May
2009, pp. 636–641.
[18] S. Jayaram, S. Schmugge, M. C. Shin, and L. V. Tsap, “Effect of colorspace
transformation, the illuminance component, and color modeling
on skin detection,” in Proc. IEEE Comput. Soc. Conf. Comput.
Vis. Pattern Recognit., 2004, vol. 2, pp. 813–818.
[19] P. Yogarajah, A. Cheddad, J. Condell, K. Curran, and P. McKevitt, “A
dynamic threshold approach for skin segmentation in color images,” in
Proc. Int. Conf. Image Process., 2010, pp. 2225–2228.
[20] P. Kakumanua, S. Makrogiannisa, and N. Bourbakis, “A survey of
skin-color modeling and detection methods,” Pattern Recognit., vol.
40, no. 3, pp. 1106–1122, 2007.
[21] J. Berens and G. Finlayson, “Log-opponent chromaticity coding of
colour space,” in Proc. Int. Conf. Pattern Recognit., Barcelona, Spain,
2000, vol. 1, pp. 206–211.
[22] E. Hering, Outlines of a Theory of the Light Sense. Cambridge, MA:
Havard Univ. Press, 1964.
[23] L. M. Hurvich and D. Jameson, “An opponent-process theory of color
vision,” Psychol. Rev., vol. 64, pp. 384–404, Nov. 1957.
[24] S. Mitra and T. Acharya, “Gesture recognition: A survey,” IEEE Trans.
Syst., Man, Cybern., C: Appl. Rev., vol. 37, no. 3, pp. 311–324, May
2007.
[25] A. M. Elgammal, C. Muang, and D. Hu, “Skin detection,” in Encyclopedia
of Biometrics. Germany, Berlin: Springer, 2009, pp.
1218–1224.
[26] I. Fasel, B. Fortenberry, and J. Movellan, “A generative framework
for real time object detection and classification,” Comput. Vis. Image
Underst., vol. 98, pp. 182–210, Apr. 2005.
[27] C. Kumar and A. Bindu, “An efficient skin illumination compensation
model for efficient face detection,” in Proc. 32nd IEEE Annu. Conf.
Ind. Electron., 2006, pp. 3444–3449.
[28] D. A. Forsyth and M. M. Fleck, “Automatic detection of human nudes,”
Int. J. Comput. Vis., vol. 32, pp. 63–77, Aug. 1999.
[29] P. H. Eilers and J. J. Goeman, “Enhancing scatterplots with smoothed
densities,” Bioinformatics, vol. 20, no. 5, pp. 623–628, 2004.
[30] J. Stottinger, A. Hanbury, C. Liensberger, and R. Khan, “Skin paths for
contextual flagging adult video,” in Proc. Int. Symp. Visual Comput.,
2009, pp. 903–906.
[31] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman,
“The pascal visual object classes challenge 2009 (VOC2009),”
2009.
[32] A. Cheddad, J. Condell, K. Curran, and P. McKevitt, “A skin tone detection
algorithm for an adaptive approach to

Keywords

  • Color space
  • dynamic threshold
  • fusion strategy
  • skin detection.

Fingerprint

Dive into the research topics of 'A Fusion Approach for Efficient Human Skin Detection'. Together they form a unique fingerprint.

Cite this