Call for Paper

CAE solicits original research papers for the July 2021 Edition. Last date of manuscript submission is June 30, 2021.

Read More

Facial Recognition based on Histogram Matching with Adaptive Threshold

Luong Anh Tuan Nguyen. Published in Image Processing.

Communications on Applied Electronics
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Luong Anh Tuan Nguyen

Luong Anh Tuan Nguyen. Facial Recognition based on Histogram Matching with Adaptive Threshold. Communications on Applied Electronics 7(8):1-5, October 2017. BibTeX

	author = {Luong Anh Tuan Nguyen},
	title = {Facial Recognition based on Histogram Matching with Adaptive Threshold},
	journal = {Communications on Applied Electronics},
	issue_date = {October 2017},
	volume = {7},
	number = {8},
	month = {Oct},
	year = {2017},
	issn = {2394-4714},
	pages = {1-5},
	numpages = {5},
	url = {},
	doi = {10.5120/cae2017652702},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Facial recognition was a field that was extensively studied in the past years but it is still an active area of research. This paper proposes a new method by matching histogram with adaptive threshold. The proposed method is simple but effective and it can be use for real-time system. Publicly available AT&T database is used for the evaluation of the proposed method, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. The proposed method provides a recognition rate higher than 99% and a verification error lower than 1%.


  1. M.A. Turk, and A.P. Pentland, Face recognition using eigenfaces, in Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 1991, pp. 586591.
  2. S. G. Karungaru, M. Fukumi, and N. Akamatsu, Face recognition in colour images using neural networks and genetic algorithms, Intl Journal of Computational Intelligence and Applications, vol. 5, no. 1, pp. 55-67, 2005.
  3. K. Kotani, Q. Chen, F. F. Lee, and T. Ohmi, Regiondivision VQ histogram method for human face recognition, Intelligent Automation and Soft Computing, vol. 12, no. 3, pp. 257-268, 2006.
  4. R. Chellapa, P. Sinha, P. J. Phillips, Face recognition by computers and humans,Computer Magazine, Vol. 43, pp. 46-55, Feb. 2010.
  5. Pierluigi Carcagni, Marco Del Coco, Marco Leo, Cosimo Distante, ”Facial expression recognition and histograms of oriented gradients: a comprehensive survey” in Springer Open Journal, pp. 1-25, 2015
  6. Alaa Eleyan, Hasan Demirel, ”PCA an LDA based face recognition using feedforward neural network classifier”, Lecture Notes in Computer Science(MRSC 06), vol.4105, pp. 199 206, Jun. 2006.
  7. R. Brunelli and T. Poggio, Face recognition: features versus templates, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 10, pp. 1042-1052, 1993.
  8. L. Wiskott, J. M. Fellous, N. Kruger, and C. Malsburg, ”Face recognition by elastic bunch graph matching,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 10, pp.775-780, 1997.
  9. Luong Anh Tuan Nguyen, Huu Khuong Nguyen. Traffic Density Identification Based On Histogram. Journal of Transportation Science and Technology, ISSN: 1859-4263, Vol 15-05/2015, pp 23-27.
  10. Xiangyun Ye, Mohamed Cheriet, Senior Member, Ching Y. Suen (2001), Stroke-Model-Based Character Extraction from Gray-Level Document Images, IEEE, 2001.
  11. C. C. Sun. S. J. Ruan, M. C. Shie, T.W. Pai, Dynamic Contrast Enhancement based on Histogram Specification, IEEE Transactions on Consumer Electronics, 51(4), pp.1300- 1305, 2005.
  12. Luong Anh Tuan Nguyen, Thi-Ngoc-Thanh Nguyen. Traffic Image Classification using Horizontal Slice Algorithm. International Journal of Computer Applications (ISSN: 0975 8887), Volume 148 No.11, pp. 30-34, August 2016.
  13. Al Bovik, Handbook of Image and Video Processing, Academic Press, 2000.
  14. Gonzalez, R., C., and Woods, R., E., 2001, Digital Image Processing, Prentice Hall, NJ, 2001
  15. AT&T Laboratories Cambridge. The database of faces. [Accessed September, 2017]. Available:
  16. C. Willmott, and K. Matsuura, Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in assessing average model performance, Clim. Res., 30, 7982, 2005.


Facial Recognition, Histogram, Histogram Matching, Cross- Correlation Coefficient, Adaptive Threshold