Automatic face recognition (AFR) technologies have seen substantial developments in performance over the past years, and such systems are now broadly used for security and mercantile applications. An automated system for human face recognition in a real-time background for an Institute to mark the attendance of the employees. So Smart Attendance using Real-Time Face Recognition is a real-world solution that comes with day to day activities of handling students/employees. The task is a bit challenging as the real-time background subtraction in an image is still a bit difficult. The proposed system maintains the attendance records of students automatically. Manual entering of attendance in logbooks becomes a difficult task and it also wastes time. So developing such an efficient module that comprises of face recognition to manage the attendance records of students. This module enrolls the student’s face. This enrolling is a onetime process and their face will be stored in the database. While we go through enrolling of face, we require a system since it is only needed to be performed once. You can have your own roll number as your student ID which will be unique for each student. The presence of each student will be updated in a database. The results obtained in this method showed improved performance over the regular manual attendance management system. Attendance is marked as per the student identification. This product gives much more useful solutions with accurate and precise results in a user-interactive manner rather than existing manual attendance and leave management systems. It is then transferred to the office through RF communication which is widely using in industries recently.

Image processing, Face Detection method, HAAR Cascade, Feature Extraction, Face Recognition Method

[1] Z.Wang, D.L.Huang, Research on Key Technology of Image Transmission Based on Arduino, Computer Technology and Development, 2015.05, pp183-186.
[2] W. Z.S, Research on the Network Layer Topology Measurement Based on SNMP, AJETR 2013.
[3] Xiao Shang Research of AS level Network Topology, AJETR- 2014.
[4] Pei, Donghui, The Design and Implementation of Serial Monitoring Software Based on Arduino, ICICEE2012.
[5] Holman R, Stanley J, O zkan-Haller T. Applying video sensor networks to nearshore environment monitoring[J].IEEE Trans. on Pervasive Computing, 2003.
[6] Akyildiz I. F., Melodia T., Chowdhury K. R., A survey on wireless multimedia sensor networks [J]. Computer Networks, 2007.
[7] Z.S., D.L. H, AMR, Research And Implementation Of Image Transmission Key Technological Based On WSN, PP 4759-4762, 2014.
[8] Chen Guoshao, The Design of Greenhouse Environment Monitoring System Based on Arduino, AJETR2014-01, PP86 -91.
[9] J. Goldstein, L. D. Harmon, and A. B. Lesk, “Identification of Human Faces,” Proc. IEEE, May 1971.
[10] L. Sirovich and M. Kirby, "A Low-Dimensional Procedure for the Characterization of Human Faces," J. Optical Soc. Am. A, 1987.
[11] M. A. Turk and A. P. Pentland, "Face Recognition Using Eigenfaces," Proc. IEEE, 1991, 586-591.
[12] Md.T. Akhtar, S.T. Razi, K.N. Jaman, A. Azimusshan, Md.A. Sohel,”Fast and Real life object detection system using simple Webcam”,ISROSET Journal (IJSRCSE)
[13] Suma S L, Sarika Raga,” Real Time Face Recognition of Human Faces by using LBPH and Viola Jones Algorithm”, ISROSET Journal (IJSRCSE).
[14] R. Chellappa, C. L. Wilson, and S. Sirohey, 1995. “Human and Machine Recognition of Faces: A Survey”, Proc.of the IEEE.