In today’s academic system, proper student
attendance management is crucial for the academic
institutions to disseminate and give quality education and
success to students. Managing the attendance of each
student can be a great burden on the teachers if they do it
manually. The traditional methods practiced in most of the
institutions include calling names of students or signing on
papers, which are highly time-consuming and insecure
methods. To overcome these problems, a smart and auto
attendance management system may be developed.
Earlier, several automated attendance systems have been
proposed based on biometric recognition, barcode, near
field communication mobile devices like Bluetooth module
attendance system and QR code. However, all these
systems are not efficient in terms of processing time, cost,
and accuracy. Face recognition is one of the biometric
methods to improve this system. The purpose of this work
is to survey several algorithms, their benefits, and their
drawbacks. We also go over the steps followed in the facial
recognition process. This review will be beneficial for
upcoming researchers for selection of appropriate
techniques for face recognition.
Machine Learning (ML), Logistic Regression (LR), Support Vector Machine (SVM), K nearest neighbour (KNN), Random Forest (RF), Principal Component Analysis (PCA), Convolutional Neural Network (CNN).