UNIV. NEWS. NORTH-CAUCAS. REG. TECHNICAL SCIENCES SERIES. 2016; 3: 24-30
http://dx.doi.org/10.17213/0321-2653-2016-3-24-30
ATTENDANCE CONTROL SYSTEM BASED ON FACE RECOGNITION ALGORITHMS
Sinetsky Roman Mikhailovich – Candidate of Technical Sciences, department «Software Engineering», Platov South-Russian State Polytechnic University (NPI), Novocherkassk, Russia. Ph. (8635)255295. E-mail: rmsin@srspu.ru
Gavrikov Mikhail Mikhailovich – Candidate of Technical Sciences, assistant professor, department «Software Engineering», Platov South-Russian State Polytechnic University (NPI), Novocherkassk, Russia. Ph. (8635)255295. E-mail: gmm1000@yandex.ru
Abstract
This work raises the questions of face detection and recognition in the annex to the task of the accounting of visit by students of classes in higher education institution. Firstly, features of the task are discussed, the general structure of system, the main stages of its functioning is offered. Further attention is paid to discussion of the most important algorithms of system: detections of faces on the image received from the camera and recognitions of these faces. For the solution of the task of detection, using Viola-Jones algorithms is discussed. The problem of recognition is solved by the LBPH method. Based on the described algorithms the prototype of system of identification of persons on the image is realized and practical tests of a prototype are carried out, conclusions are drawn. Experiments show the good results confirming prospects of system for the solution of the task of the accounting of visits of occupations by students.
Keywords: face recognition; face detection; image processing; pattern recognition and processing; algorithm Viola –Jones; algorithm LBPH.
Full text: [in elibrary.ru]
References
1. Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About–URL: http://web.mit.edu/sinhalab/Papers/19results_sinha_etal.pdf
2. Stan Z. Li, Anil K. Jain. Handbook of Face Recognition. Second edition. – Springer-Verlag,
3. Face Detection–URL: https://facedetection.com/
4. Paul Viola, Michael J. Jones. Rapid Object Detection using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. URL: ttps://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-cvpr-01.pdf
5. Rainer Lienhart, Alexander Kuranov, Vadim Pisarevsky. Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection. IEEE ICIP 2002 – URL: http://www.multimedia-computing.de/mediawiki//images/5/52/MRL-TR-May02-revised-Dec02.pdf
6. Yoav Freund, Robert E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 1997, no. 55(1), pp. 119–139.
7. Turk and M., Pentland A. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 1991, vol. 3, no. 1, pp. 71–86.
8. Etemad K., Chellappa R. Discriminant analysis for recognition of human face images. Journal of the Optical Society of
9. Yang J., Zhang D., Frangi A.F. Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, vol. 26, no. 1, pp. 131–137.
10. Ojala T., Pietikainen M., Harwood D. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 1996, vol. 29, no. 1, pp. 51–59.
11. Ahonen T., Hadid A., Pietikainen M. Face recognition with local binary patterns. In: Proceedings of the European Conference on Computer Vision
12. Zhao W., Chellappa R., Phillips P.J., Rosenfeld A. Face recognition: A literature survey. ACM Computing Surveys, 2003, vol. 35, no. 4, pp. 399–458.