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.
Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning
