Modeling Face Detection Application Using Convolutional Neural Network and Face-API for Effective and Efficient Online Attendance Tracking

Abstract

The pandemic of Covid-19 emergency has ended, but it gives us a new lifestyle every aspect of life and also in the education aspect has changed. At that moment as one of the ways to prevent pandemic infection, many governments give the policy to close the offline class and continue with online classes. The online class system encountered several problems and one of those problems was to track the students’ attendance to ensure all the students were attending the class. The teacher needed extra effort to track it because they needed to call the students one by one which is wasting time and sometimes would miss the presence of the students who attend the class. To make it effective efficient accurate and time-consuming when tracking attendance in online classes for teachers, we proposed the face detection model which combines face-api.js and CNN to detect and recognize the students’ faces to help teachers track attendance by just uploading the screenshot image of the online meeting application. We tested our model with accuracy and speed testing. With 3 images of every student’s face as training data, our model was able to recognize the face with 100% accuracy in just 41,65 seconds which is faster than calling students one by one that need almost 3 to 5 minutes if there are many students. Future research can be done by focusing research on improving the model to detect the students’ faces with different brightness, contrast, and saturation because students may not have the same place and condition when joining an online meeting class.