Threshold Based Skin Color Classification


  • Sasan Karamizadeh Advanced Informatics School (AIS), Universiti Teknologi Malaysia (UTM), Kuala Lumpur, 54100, Malaysia.
  • Shahidan M Abdullah Advanced Informatics School (AIS), Universiti Teknologi Malaysia (UTM), Kuala Lumpur, 54100, Malaysia.
  • Jafar Shayan Advanced Informatics School (AIS), Universiti Teknologi Malaysia (UTM), Kuala Lumpur, 54100, Malaysia.
  • Parham Nooralishahi Department of Computer Science and Information Technology, University of Malaya (UM), Kuala Lumpur, 50603, Malaysia
  • Behnaz Bagherian Faculty of Computer Science and Information Technology, University Putra Malaysia (UPM).


Skin Segmentation, Image Noise, K-NN, Multi Skin,


In this paper, we presented a new formula for skin classification. The proposed formula can overcome sensitivity to noise. Our approach was based multi-skin color Hue, Saturation, and Value color space and multi-level segmentation. Skin regions were extracted using three skin color classes, namely the Caucasoid, Mongolid and Nigroud. Moreover, in this formula, we adopted Gaussian-based weight k-NN algorithm for skin classification. The experiment result shows that the best result was achieved for Caucasoid class with 84.29 percent fmeasure.


Karamizadeh S., Abdullah S. M., Zamani M., and Kherikhah A., 2015. Pattern Recognition Techniques: Studies on Appropriate

Classifications. in Advanced Computer and Communication Engineering Technology, ed: Springer. 791-799.

K. Sasan, A. Shahidan M, M. Azizah A, Z. Mazdak, and H. Alireza, "An Overview of Principal Component Analysis," Journal of Signal and Information Processing, vol. 4, p. 173, 2013.

Karamizadeha S., Mabdullahb S., Randjbaranc E., and Rajabid M. J., 2015. A Review on Techniques of Illumination in Face Recognition. Technology. 3: 79-83.

Karamizadeh F., 2015. Face Recognition by Implying Illumination Techniques–A Review Paper. Journal of Science and Engineering. 6:001-007.

Karamizadeh S., Abdullah S. M., and Zamani M., 2013. An Overview of Holistic Face Recognition. IJRCCT. 2:738-741.

Karamizadeh S., Abdullah S. M., Halimi M., Shayan J., and javad Rajabi M., Advantage and Drawback of Support Vector Machine Functionality.

Abdullah S. M. and Manaf A. A., 2010. Multiple Layer Reversible Images Watermarking Using Enhancement Of Difference Expansion Techniques. in Networked Digital Technologies, ed: Springer. 333-342.

Muhammad G., Hussain M., Alenezy F., Bebis G., Mirza A. M., and Aboalsamh H., 2012. Race Recognition From Face Images Using Weber Local Descriptor. in Systems, Signals and Image Processing (IWSSIP), 2012 19th International Conference on. 421-424.

Tanaka J. W.andPierce L. J., 2009. The Neural Plasticity Of OtherRace Face Recognition. Cognitive, Affective, & Behavioral Neuroscience. 9:122-131.

Gupta A. and Chaudhary A., 2014. Robust Skin Segmentation using Color Space Switching. Pattern Recognition and Image Analysis.

Zhao S., Song X., Tan W., and Li H., 2010. A novel approach to hand gesture contour detection based on GVF Snake model and skin color elliptical model. in Computer Application and System Modeling (ICCASM). 2010 International Conference on. V5-381-V5-384.

Khan R., Hanbury A., Stöttinger J., and Bais A., 2012. Color Based Skin Classification, Pattern Recognition Letters. 33:157-163.

Roomi S. M. M., Virasundarii S., Selvamegala S., Jeevanandham S., and Hariharasudhan D., 2011. Race Classification Based on Facial Features. in Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2011 Third National Conference on. 54-57.

Duda R. O., Hart P. E., and Stork D. G., 2012. Pattern classification: John Wiley & Sons.

Phung S. L., Bouzerdoum A., and Chai Sr D., 2005. Skin Segmentation Using Color Pixel Classification: Analysis And Comparison. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 27: 148-154.

Prasad S., Sawant A., Shettigar R., Bhandari K., and Sinha S., 2011. Skin Segmentation Based Face Tracking Independent Of Lighting Conditions. in Proceedings Of The International Conference & Workshop On Emerging Trends In Technology. 123-126.

Jang C. Y., Hyun J., Cho S., Kim H.-S., and Kim Y. H., 2012. Adaptive Selection of Weights in Multi-scale Retinex using Illumination and Object Edges. ed: IPCV.

Bagirov A. M., Ugon J., and Webb D. 2011. An Efficient Algorithm For The Incremental Construction Of A Piecewise Linear Classifier. Information Systems. 36: 782-790.

Nusirwan A., Wei K., and See J., 2011. RGB-H-CbCr Skin Colour Model for Human Face Detection.ed.

Dixit M., Rasiwasia N., and Vasconcelos N., 2011. Adapted Gaussian Models For Image Classification. in Computer Vision and Pattern Recognition (CVPR). 2011 IEEE Conference on. 937-943.

Subban R.and Mankame D. P., 2014. Human Face Recognition Biometric Techniques: Analysis and Review. in Recent Advances in Intelligent Informatics, ed: Springer. 455-463.

Srivastava C., Mishra S. K., Asthana P., Mishra G., and Singh O., 2013. Performance Comparison of Various Filters and Wavelet Transform for Image De-Noising‖. IOSR Journal of Computer Engineering, eISSN. 2278-0661.

Yang A. Y., Zhou Z., Balasubramanian A. G., Sastry S. S., and Ma Y., 2013. Fast-minimization algorithms for robust face recognition. Image Processing, IEEE Transactions on. 22: 3234-3246.

Rudovic O., Pantic M., and Patras I., 2013. Coupled Gaussian Processes For Pose-Invariant Facial Expression Recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 35:1357-1369.

Saleh Al-amri S., Kalyankar N., and Khamitkar S., 2010. A Comparative Study of Removal Noise from Remote Sensing Image.

arXiv preprint arXiv:1002.1148.

Mishra S. K., Ahmad K., Trivedi A., Shukla M., and Pandey H., 2013. Image de-noising using wavelet thresholding method. International Journal of Advanced Scientific and Technical Research.

Bhatia, N. 2010. Survey of nearest neighbor techniques. arXiv preprint arXiv:1007.0085.




How to Cite

Karamizadeh, S., M Abdullah, S., Shayan, J., Nooralishahi, P., & Bagherian, B. (2017). Threshold Based Skin Color Classification. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-3), 131–134. Retrieved from