Abstract
Finding facial component in face images is a significant arrangement for various facial image-understanding applications. The face detection is a process of detecting a region of the face from a picture, or image of one or multiple objects together. In this paper, we introduce Adaboost, viola-Jones, and Haar algorithms to detect faces either through mobile phone interface or from desktop computer UI. The application of the work has extended into an automated classroom attendance system using a handheld device. The results have shown the effectiveness of the proposed model.
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References
Shehu, V., Dika, A.: Using real-time computer algorithms in automatic attendance management systems, pp. 397–402. IEEE, June 2010
Kumar, K.S., Semwal, V.B., Tripathi, R.C.: Real-Time face recognition using adaboost improved fast PCA algorithm. Int. J. Artif. Intell. Appl. 2(3), 45–58 (2011)
Crosswhite, N., Byrne, J., Stauffer, C., Parkhi, O., Cao, Q., Zisserman, A.: Template adaptation for face verification and identification. Image Vis. Comput. 79, 35–48 (2018)
Mahvish, N.: Face Detection and Recognition. Few Tutorials (2014)
Okokpujie, K., Noma-Osaghae, E., John, S., Grace, K.A., Okokpujie, I.: A face recognition attendance system with GSM notification. In: 2017 IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON), pp. 239–244. IEEE, November 2017
Tom, N.: Face Detection, Near Infinity - Podcasts (2007)
Bhattacharya, S., Nainala, G.S., Das, P., Routray, A.: Smart attendance monitoring system (SAMS): a face recognition based attendance system for classroom environment. In: 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT), pp. 358–360. IEEE, July 2018
Wang, Y.-Q.: An analysis of the Viola-Jones face detection algorithm. Image Process. Line 4, 128–148 (2014)
Freund, Y., Schapire, R., Abe, N.: A short introduction to boosting. J. Soc. Artif. Intell. 14(771–780), 1612 (1999)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Fuzail, M., Nouman, H.M.F., Mushtaq, M.O., Raza, B., Tayyab, A., Talib, M.W.: Face Detection System for Attendance of Class’ Students (2014)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Computational Learning Theory, pp. 23–37 (1995)
OpenCV Template Matching Documentation, improve module-Image Processing. https://docs.opencv.org/2.4/doc/tutorials/imgproc/histograms/template_matching/template_matching.html. Accessed 12 June 18
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Bong, C.W., Xian, P.Y., Thomas, J. (2020). Face Recognition and Detection Using Haars Features with Template Matching Algorithm. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2019. Advances in Intelligent Systems and Computing, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-33585-4_45
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DOI: https://doi.org/10.1007/978-3-030-33585-4_45
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