Development of a facial recognition system with email identification message relay mechanism

Abstract

Abstract-Attendance records play a vital role in the educational sector. It is so vital that students are not allowed to sit for examinations if they do not meet the class attendance benchmark. But students, instead of making sure they attend classes regularly, devise cunny ways of committing attendance fraud. This unpleasant trend has made it necessary to develop systems that can take accurate class attendance records and minimize fraud. The use of biometrics to develop attendance taking systems is becoming quite popular. One of such biometrics is The Face. In this paper, a facial recognition algorithm known as Fisherfaces or Fisher Discriminant Analysis (FDA) which is not sensitive to substantial variation in facial look and illumination is used to develop the facial recognition attendance taking system. The system implemented has a training database of Ten (10) students. Ten (10) facial images of each student are taken with different composures, looks and under different levels of illumination. Tests on nine (9) students in the database yielded accuracies of as low as 70% and as high as 90%. This validates the proof that the more the number of training facial image in the database, the higher the accuracy of Fisherfaces approach. The simple mail transfer protocol (SMTP) was interfaced with the database to send identification messages (name of student identified with time and date of identification) to the email address of the administrator (in this case the lecturer) in realtime to effectively monitor the attendance. The result was found capable of eliminating attendance fraud.

Artificial Intelligence Techniques for Distance Education: A Systematic Literature Review

“…(Li & Wang, 2018) [9] Fisher Discriminant Analysis (FDA) This study aims to develop systems for recording attendance and absence for students through the use of biometrics as face recognition systems, and the goal of developing this system was to eliminate fraud in attendance and absence. (Okokpujie et al, 2017) [33]…”

“…The system achieved accuracy in verifying the ratio from 70% to 90%. This system made it clear that the more images in the database, the greater the accuracy [33].…”

“…Many of the systems depend on the databases created by the researchers to achieve their systems and train it, as Ngoc Anh collected his data by taking snapshots of students in lessons (the subject of basic programming) at FPT University [30]. Other researchers also recorded pictures of students in different poses with many facial expressions and collected them in the databases of their systems [31], [33], [35].…”

“…1) The student's sitting position and the angles of the body are difficult to explain whether the body is tilted or otherwise to judge if the student watches the video online or not [4], [30]. 2) Lack of categorized reactions and feelings, as one of the expressions that do not find any similar classification which may appear [33], [35]. 3) Facial expressions vary very quickly and with many different feelings [33].…”

“…2) Lack of categorized reactions and feelings, as one of the expressions that do not find any similar classification which may appear [33], [35]. 3) Facial expressions vary very quickly and with many different feelings [33]. 4) The glasses worn by the student may reduce the detection of emotions [5].…”