Face Recognition Using Facenet Deep Learning Network for Attendance System

Abstract

Face recognition that is technology used for recognizing human faces based on certain patterns and re-detect faces in various conditions. Face recognition is currently becoming popular to be applied in various ways, especially in security systems. Various methods of face recognition have been proposed in researches and increased accuracy is the main goal in the development of face recognition methods. FaceNet is one of the new methods in face recognition technology. This method is based on a deep convolutional network and triplet loss training to carry out training data, but the training process requires complex computing and a long time. By integrating the Tensorflow learning machine and pre-trained model, the training time needed is much shorter. This research aims to conduct surveys, test performance, and compare the accuracy of the results of recognizing the face of the FaceNet method with various other methods that have been developed previously. Implementation of the FaceNet method in research using two types of pre-trained models, namely CASIA-WebFace and VGGFace2, and tested on various data sets of standard face images that have been widely used before. From the results of this research experiment, FaceNet showed excellent results and was superior to other methods. By using VGGFace2 pre-trained models, FaceNet is able to touch 100% accuracy on YALE, JAFFE, AT & T datasets, Essex faces95, Essex grimace, 99.375% for Essex faces94 dataset and the worst 77.67% for the faces96 dataset.