Feature Learning Approach for Facial Recognition Using Deep Metric Learning

Abstract

Standard deep learning in the context of facial recognition involves inputting a single image and outputting a label for that image. Deep metric learning distinguishes itself by outputting a real valued feature vector instead of a single label. The usage of deep metric learning has revolutionised facial recognition, making it very accurate and reliable. This paper exhibits the accuracy and reliability of the facial recognition model using deep metric learning in the application of an automated attendance system. The paper presents a non-intrusive attendance system which uses the described neural network to recognize faces and record attendance. The system uses the pre-trained neural network to generate embeddings for faces, using a method known as the triple training step, which is described in the paper. These embeddings are generated from a collection of photos per person. After the embeddings are generated, the system is ready to perform facial recognition on sample photos. CNN is used for facial detection in the sample group photos. Once the faces are detected, a KNN classifier is used for recognizing faces. Finally after the faces are recognized, the attendance for each recognized student is marked in the database. Thus, the whole process of attendance was automated without the requirement of human interaction.

Application of CNN based Regognition Technology in Public English Teaching in College Intelligent Classroom

“…For the purpose of improving the efficiency of face recognition, Karkal G and other researchers built a non-invasive attendance system, which used CNN to detect faces and a neural network to recognize faces. The result showed that the system realized automatic detection of face recognition and improved the efficiency of attendance [11]. For the purpose of improving the efficiency of classroom attendance, Seelam V and others proposed an intelligent classroom attendance management system based on face recognition by using the principles of deep learning and computer vision.…”