Custom Deep Learning Face Recognition based on Tensorflow Model for Attendance Management System

Face recognition technology has many implementation roles in the attendance management system. Attendance systems need proper solutions to detect a face in real-time situations using a particular purpose device. Face recognition systems can differentiate human faces based on face features trained in the deep learning model. Although significant advances in face recognition can increase the variety of face conditions in detection, some challenges still exist in face recognition that makes the existing model need to be taken apart and reparametrized. Challenge comes from lighting condition, blurred condition, also face tilt position. This research design a superficial layer of convolutional neural network using a built-in Tensorflow sequential model library. Generally, a transfer learning mechanism is used in object detection, especially in face recognition. This research doesn't use transfer learning because the accuracy of an existing model like the InceptionV1 model gives good accuracy in cross-validation training but gives a significant error in testing the trained face. The attendance management system was built in Flask Web Framework because developed in a Python language environment. The accuracy of the custom model has an average of 88.23%, which is tested with 16 different students, with each student having 48 pictures.