Three stage robust attendance recording and monitoring using social media platform, image processing and cloud computing

Within the ambit of this paper, we propose a system that takes attendance of students for classroom lecture with a three stage robust mechanism for precise attendance recording with high accuracy and weeding out proxies completely. These days it is easily possible to estimate automatically the number of students present or absent and using image processing, each student can be identified. The solutions available are however, heavily hardware dependent like RFID or Bluetooth technology and are not tamper proof. We propose a method that takes attendance by tracking the cell phones of students, counts the number of students in order to check duplicity of devices and in the third stage identifies the registered users individually to detect impersonation. The robust algorithm will be very effective in controlling the cases of cheating by impersonation in entrance examinations. In the proposed system, firstly, an Arduino based hardware system scans for registered Wi-Fi devices. Secondly, a face detection system counts the number of students in the class. Finally, a face recognition system that employs Facebook's API can tag students in real time on dedicated Facebook pages for a particular class. This way the attendance taking process is streamlined & proxies completely removed.