Optimising Face Recognition System using Contrastive Learning and Contrastive Loss

The current issue with facial recognition systems is that they can be influenced by poor illumination or inadequate image quality. Because camera angles are obstructed, the data may not match the person's nodal point. There are many more difficulties, such as variations in facial appearances such as illumination, pose, expression, and aging. To tackle this difficulty, this study has developed a new strategy by utilizing contrastive learning for face recognition system. Contrastive learning is a Machine Learning (ML) approach that integrates information from two faces to understand the unique qualities of an individual's face. Contrastive learning improves the system's vision by generating two vectors, positive and negative, by varying the image contrast. The model is then trained to learn about images and recognize them more efficiently and effectively by establishing those two vectors.