A Fusion Approach for Efficient Human Skin Detection

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

Research output: Contribution to journalArticle

97 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.
LanguageEnglish
Pages138-147
JournalIEEE Transactions on Industrial Informatics
Volume8
Issue number1
DOIs
Publication statusPublished - 1 Feb 2012

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Skin
Fusion reactions
Color
Lighting
Costs
Detectors

Keywords

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

Cite this

@article{2a40ebcd1ed94f53b6cb687f637a9215,
title = "A Fusion Approach for Efficient Human Skin Detection",
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.",
keywords = "Color space, dynamic threshold, fusion strategy, skin detection.",
author = "Tan, {Wei Ren} and Chan, {Chee Seng} and Pratheepan Yogarajah and Joan Condell",
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",
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}

A Fusion Approach for Efficient Human Skin Detection. / Tan, Wei Ren; Chan, Chee Seng; Yogarajah, Pratheepan; Condell, Joan.

In: IEEE Transactions on Industrial Informatics, Vol. 8, No. 1, 01.02.2012, p. 138-147.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A Fusion Approach for Efficient Human Skin Detection

AU - Tan, Wei Ren

AU - Chan, Chee Seng

AU - Yogarajah, Pratheepan

AU - Condell, Joan

N1 - 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

PY - 2012/2/1

Y1 - 2012/2/1

N2 - 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.

AB - 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.

KW - Color space

KW - dynamic threshold

KW - fusion strategy

KW - skin detection.

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DO - 10.1109/TII.2011.2172451

M3 - Article

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SP - 138

EP - 147

JO - IEEE Transactions on Industrial Informatics

T2 - IEEE Transactions on Industrial Informatics

JF - IEEE Transactions on Industrial Informatics

SN - 1551-3203

IS - 1

ER -