A Comparison of the YCBCR Color Space with Gray Scale for Face Recognition for Surveillance Applications

  • Jamal Ahmad Dargham
    UNIVERSITI MALAYSIA SABAH jamalad[at]ums.edu.my
  • Ali Chekima
  • Ervin Gubin Moung
  • Segiru Omatu
    Osaka Institute of Technology


Face recognition is an important biometric method because of its potential applications in many fields, such as access control and surveillance. In this paper, the performance of the individual channels from the YCBCR colour space on face recognition for surveillance applications is investigated and compared with the performance of the grayscale. In addition, the performance of fusing two or more colour channels is also compared with that of the grayscale. Three cases with a different number of training images per persons were used as a test bed. It was found out that, the grayscale always outperforms the individual channel. However, the fusion of CBxCR with any other channel outperforms the grayscale when three images of the same class from the same database are used for training. Regardless of the cases used, the CBxCR channel always gave the best performance for the individual colour channels. It was found that, in general, increasing the number of fused channels increases the performance of the system. It was also found that the grayscale channel is the better choice for face recognition since it contains better quality edges and visual features which are essential for face recognition.
  • Referencias
  • Cómo citar
  • Del mismo autor
  • Métricas
Chaves-González, J. M., Vega-Rodríguez, M. A., Gómez-Pulido, J. A., Sánchez-Pérez, J. M., 2010. Detecting skin in face recognition systems A colour spaces study, Digital Signal Processing, Volume 20, Issue 3, pages 806-823. - https://doi.org/10.1016/j.dsp.2009.10.008

Chelali, F.Z., Cherabit, N., Djeradi, A., 2015. Face recognition system using skin detection in RGB and YCBCR color spa-ce, Web Applications and Networking (WSWAN), 2nd World Symposium, pages 1-7. - https://doi.org/10.1109/WSWAN.2015.7210329

Dargham, J., Chekima, A., Moung, E., and Omatu, S, 2015. The Effect of Training Data Selection on Face Recognition in Surveillance Application, Advances In Distributed Computing And Artificial Intelligence Journal, Volume 3, pa-ges 58-66. - https://doi.org/10.14201/ADCAIJ2014345866

Dargham, J., Chekima, A., and Moung, E., 2012. Fusion of PCA and LDA Based Face Recognition System, International Conference on Software and Computer Applications, IPCSIT Volume 41.

Karimi, B. and Devroye, L., 2007. A Study on Significance of Color in Face Recognition using Several Eigenface Algo-rithms, Canadian Conference Electrical and Computer Engineering (CCECE), pages 1309-1312. - https://doi.org/10.1109/CCECE.2007.333

National ICT Australia Limited, 2014. http://arma.sourceforge.net/chokepoint/

Rethunk, 2012. Why we should use gray scale for image processing? [Online forum comment]. Message posted to http://stackoverflow.com/questions/12752168/why-we-should-use-gray-scale-for-image-processing

Turk, M. and Pentland, A., 1991. Eigenfaces for Recognition, Journal of Cognitive Neuroscience, Volume 3, pages 71-86. - https://doi.org/10.1162/jocn.1991.3.1.71

Yoo, S., Park, R. and Sim, D., 2007. Investigation of Color Spaces for Face Recognition, In Proceedings of Machine Vi-sion Application, pages 106-109.

Zhang, J., 2012. Computer Vision: If you had to choose, would you rather go without luminance or chrominance? [Onli-ne forum comment]. Message posted to https://www.quora.com/Computer-Vision/Computer-Vision-If-you-had-to-choose-would-you-rather-go-without-luminance-or-chrominance/answer/John-Zhang

Zhao, W., Chellappa, R., Phillips, P. J., Rosenfeld, A., 2003. Face recognition: A literature survey, ACM Computing Sur-veys (CSUR), Volume 35, Issue 4, pages 399-458. - https://doi.org/10.1145/954339.954342
Dargham, J. A., Chekima, A., Moung, E. G., & Omatu, S. (2018). A Comparison of the YCBCR Color Space with Gray Scale for Face Recognition for Surveillance Applications. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 7(2), 43–52. https://doi.org/10.14201/ADCAIJ2018724352


Download data is not yet available.

Author Biographies

Jamal Ahmad Dargham

Faculty of Engineering

Ali Chekima

Faculty of Engineering

Ervin Gubin Moung

Faculty of Engineering

Segiru Omatu

Osaka Institute of Technology
Faculty of Engineering, Department of Electronics, Information and Communication Engineering