Call for Paper - November 2023 Edition
IJCA solicits original research papers for the November 2023 Edition. Last date of manuscript submission is October 20, 2023. Read More

Face Recognition System for Students’ Attendance Register

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2019
Mustafa M. Eid, Amany F. El-gamal, Elsaeed E. Abd Elrazek

Mustafa M Eid, Amany F El-gamal and Elsaeed Abd E Elrazek. Face Recognition System for Students’ Attendance Register. International Journal of Computer Applications 177(10):23-28, October 2019. BibTeX

	author = {Mustafa M. Eid and Amany F. El-gamal and Elsaeed E. Abd Elrazek},
	title = {Face Recognition System for Students’ Attendance Register},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2019},
	volume = {177},
	number = {10},
	month = {Oct},
	year = {2019},
	issn = {0975-8887},
	pages = {23-28},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2019919487},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Face recognition is very important topic because of its applications. The purpose of this work is developing a system that can recognize a person and register postgraduate students' attendance at faculty of specific education, Damietta University, Egypt. The proposed system consists of five stages: image acquisition, face detection, pre-processing, features extraction and classification. Image acquisition to capture real-time images. Face detection to detect face region from the image. Pre-processing stage involve the effective way of suppressing the unwanted distortion of image. Feature Extraction is a method of capturing visual content of images such as extraction of color, texture. In this work, Gray Level Co-occurrence Matrix is used for calculating texture features of the image. Four features namely, Angular Second Moment, Correlation, Inverse Difference Moment and Contrast are computed. Four classifiers were used: KNN (Nearest Neighbor), Naïve Bayes, Decision Tree and Discriminant Analysis. The accuracy of KNN is better than other classifiers therefore, KNN is used. The performance of the proposed system is evaluated by using dataset of postgraduate students' faces. Experimental results show that the proposed system achieved accurately of 90%.


  1. S. Yamuna, S. Abirami, "Feature Extraction of Face Value Through Gray-Level Co-Occurence Matrix", International Journal of Open Information Technologies, ISSN: 2307-8162 Vol. 3, No. 6, 2015.
  2. H. Sellahewa, S. Jassim, "A survey on Face Detection Methods and Feature Extraction Techniques of Face Recognition", International Journal of Emerging Trends & Technology in Computer Science, Vol. 3, Issue 3, ISSN 2278-6856.2014.
  3. K. Dinesh, M. Rajni, "Face Recognition Based on PCA Algorithm Using Simulink in Matlab", International Journal of Advanced Research in Computer Engineering & Technology, Vol. 3, Issue 7, 2014.
  4. A. Kumar, R. Ranjan, "An Algorithm for Face Detection and Feature Extraction", International Journal of Science, Engineering and Technology Research, Vol. 3, Issue 4, 2014.
  5. D. Tian, "A Review on Image Feature Extraction and Representation Techniques", International Journal of Multimedia and Ubiquitous Engineering, Vol. 8, No. 4, 2013.
  6. N. Sharma, A. Ray, and others. "Segmentation and classification of medical images using texture-primitive features", Journal of medical physics, Vol. 33, Issue 3, PP. 119-126, 2008.
  7. N. Bouhlel, S. Sevestre, "Markov random field as texture model for ultrasound RF envelope image", Computers in Biology and Medicine, Vol. 39, Issue 6, June, PP. 535- 544, 2009.
  8. P. Koltsov, "Comparative study of texture detection and classification algorithms", Computational Mathematics and Mathematical Physics, Vol. 51, No. 8, PP. 1460-1466, 2011.
  9. Du, S. Ward, R. 2005. Wavelet-based illumination normalization for face recognition", IEEE. Int Conf., Vol. 2, PP. 954-957.
  10. T. Ahmed, R. Mohamed, and others. "A Proposed OCR Algorithm for the Recognition of Handwritten Arabic Characters", Journal of Pattern Recognition and Intelligent Systems, Vol. 2, Iss. 1, PP. 90-104, 2014.
  11. P. Janani, J. Premaladha, and others. "Image Enhancement Techniques: A Study", Indian Journal of Science and Technology, Vol. 8, No. 22, ISSN (Print): 0974-6846 ISSN (Online): 0974-5645, 2015.
  12. Gonzalez, R. and Woods, R. 2008. Digital Image Processing. Pearson Education, Inc., 3nd edition.
  13. Hoke, J. 2017 A wavelet-based framework for efficient processing of digital imagery with an application to helmet-mounted vision systems. Doctoral Thesis. Computer Engineering, University of Iowa.
  14. A. Elgamal, M. Eisa, and others. "Effective medical image retrieval technique based on texture features", International Journal of Intelligent Computing and Information Science, Vol.13, No. 2, 2013.
  15. P. Mohanaiah, P. Sathyanarayana, and others. "Image Texture Feature Extraction Using GLCM Approach", International Journal of Scientific and Research Publications, Vol. 3, Issue 5, 2013.
  16. A. Suresh, K. Shunmuganathan, "Image Texture Classification using Gray Level Co-Occurrence Matrix Based Statistical Features", European Journal of Scientific Research, ISSN 1450-216X., Vol.75, No.4, PP. 591-597,2012.
  17. V. Kumar, S. Sudhanshu, and others. “Texture feature extraction and classification using radial basis function for diagnosis of brain tumour", Journal of Electronics and Communications, ISSN 0973-7006., Vol.3, PP. 161-168, 2016.
  18. Arockia, G., Saroja, S. and others. 2013. Texture analysis of non-uniform images using GLCM. Proceedings of IEEE Conference on Information and Communication Technologies, pp. 1319-1322.
  19. Guru, D., Sharath, Y., and others. 2010 "Texture Features and KNN in Classification of Flower Images", IJCA Special Issue on “Recent Trends in Image Processing and Pattern Recognition.
  20. Chatterjee, S., Sarkar S., and others. 2016 |"Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings", Natural Computing Applications.
  21. M. Hossin, M. Sulaiman, "A review on Evaluation Metrics for Data Classification Evaluations", International Journal of Data Mining & Knowledge Management Process, Vol.5, No.2, 2015.


Face Recognition, Texture Classification, K-Nearest Neighbor, Gray Level Co-occurrence Matrix.