A Modified Joint Geometrical and Statistical Alignment Approach for Low-Resolution Face Recognition

Abstract

Domain Adaptation (DA) or Transfer Learning (TL) makes use of the already available source domain information for training the target domain classifier. Traditional ML algorithms require abundant amount of labeled data for training the model, and also they assume that both training and testing data follow similar distributions. However, in a real-world scenario, this does not always work. The scarcity of labeled data in the target domain is a big issue. Also, the source and the target domains have distinct data distributions. So, lessening the gap between the distributions of the two domains is very important so that a model that is trained using source domain information can be deployed to classify the target domain information efficiently. The already existing domain adaptation technique tries to reduce this distribution interval statistically and geometrically to an extent. Nevertheless, it requires some important components such as Laplacian regularization and maximizing source domain variance. Hence, we propose a Modified Joint Geometrical and Statistical Alignment (MJGSA) approach for Low-Resolution Face Recognition that enhances the previous transfer learning methods by incorporating all the necessary objectives that are useful for diminishing the distribution gap between the domains. Rigorous experiments on several real-world datasets verify that our proposed MJGSA approach surpasses other state-of-the-art existing methods.