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A Review Paper on Automatic Attendance System using Face Detection

Mr. Prince Tiwari


Attendance marking in a classroom is a very time consuming task. It is a very hard for lecturers to take attendance in a class of very large number of students. This also reduces the time of lecture. These images are compared using SURF matching algorithm with the stored images of students. These two algorithms are implemented in MATLAB. The system can be operated automatically or manually. The focus is to make a fully automatic system which works on basis of time-table of class-room. We make a standalone application for this automatic attendance system which can work on any 64-bit computer with no need of MATLAB software.

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Viola, Paul, Michael J. Jones. Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2001; 1: 511–518p.

Xiaopeng Wang, Lucheng Wang, Tao Lei, Chengyi Wang. Face Detection Based on Improved Skin Model and Local Iterated Conditional Modes. IEEE Conference on Natural Computation (ICNC). 2015.

Zulhadi Zakaria and Shahrel A. Suandi. Detection Using Combination of Neural Network and Adaboost. School of Electrical and Electronics Engineering Universiti Sains Malaysia.

Alahi, Alexandre, Ortiz, Raphael, Pierre Vandergheynst. FREAK: Fast retina keypoint. IEEE Conference on Computer Vision and Pattern Recognition. 2012.

Bay, H., A. Ess, T. Tuytelaars, L. Van Gool. SURF: Speeded up robust features." computer vision and image understanding (CVIU). 2008; 110(3): 346–359p

M. Turk and A. Pentland Eigenfaces for recognition. Journal of Cognitive Neuroscience. 1991; 3: 71-86p.

Face recognition using eigenfaces. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition. 1991.

Mingfang DU et al. Robot robust object recognition based on fast

SURF feature matching. In Chinese Automation Congress (CAC); 2013.

Lienhart R., Kuranov A., V. Pisarevsky. Empirical analysis of detection cascades of boosted classifiers for rapid object detection. Proceedings of the 25th DAGM Symposium on Pattern Recognition. Magdeburg, Germany; 2003.

Castrillón Marco, Déniz Oscar, Guerra Cayetano, Hernández Mario. ENCARA2: Real-time detection of multiple faces at different resolutions in video streams. Journal of Visual Communication and Image Representation. 2007; (18)2: 130-140p.

Dalal, N., B. Triggs. Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005; 1: 886–893p.

ZdenekKalal, KrystianMikolajczyk, Jiri Matas. Forward-backward error: automatic detection of tracking failures. International Conference on Pattern Recognition; 2010.


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