  The current approach to marking attendance in colleges is tedious and time
consuming. I propose AttenFace, a standalone system to analyze, track and grant
attendance in real time using face recognition. Using snapshots of class from
live camera feed, the system identifies students and marks them as present in a
class based on their presence in multiple snapshots taken throughout the class
duration. Face recognition for each class is performed independently and in
parallel, ensuring that the system scales with number of concurrent classes.
Further, the separation of the face recognition server from the back-end server
for attendance calculation allows the face recognition module to be integrated
with existing attendance tracking software like Moodle. The face recognition
algorithm runs at 10 minute intervals on classroom snapshots, significantly
reducing computation compared to direct processing of live camera feed. This
method also provides students the flexibility to leave class for a short
duration (such as for a phone call) without losing attendance for that class.
Attendance is granted to a student if he remains in class for a number of
snapshots above a certain threshold. The system is fully automatic and requires
no professor intervention or any form of manual attendance or even camera
set-up, since the back-end directly interfaces with in-class cameras. AttenFace
is a first-of-its-kind one-stop solution for face-recognition-enabled
attendance in educational institutions that prevents proxy, handling all
aspects from students checking attendance to professors deciding their own
attendance policy, to college administration enforcing default attendance
rules.
