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

Ervin Gubin MOUNG

Abstract


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 color space on face recognition for surveillance applications is investigated and compared with the performance of the gray scale. In addition, the performance of fusing two or more color channels is also compared with that of the gray scale. Three cases with different number of training images per persons were used as a test bed. It was found out that, the gray scale always outperforms the individual channel. However, the fusion of CBxCR with any other channel outperforms the gray scale 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 color 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 gray scale channel is the better choice for face recognition since it contain better quality edges and visual features which are essential for face recognition.

Keywords


Principal Component Analysis; YCBCR color space; Face recognition; Surveil-lance applications

Full Text:

PDF

References


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.

Chelali, F. Z., Cherabit, N., Djeradi, A., 2015. Face recognition system using skin detection in RGB and YCBCR color space, Web Applications and Networking (WSWAN), 2nd World Symposium, pages 1-7.

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, pages 58-66.

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 Algorithms, Canadian Conference Electrical and Computer Engineering (CCECE), pages 1309-1312.

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.

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

Zhang, J., 2012. Computer Vision: If you had to choose, would you rather go without luminance or chrominance? [Online 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 Surveys (CSUR), Volume 35, Issue 4, pages 399-458.




DOI: http://dx.doi.org/10.14201/ADCAIJ2017642533





Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.