A Comparative Study Between Class-Dependent and Class-Independent Algorithms of Linear Discriminant Analysis: A case study- Face Recognition

Face recognition is the process of identifying an individual by analyzing their facial features. It has become an essential area of research due to its numerous practical applications, such as security systems, access control, and surveillance. One of the challenges in face recognition is dealing with high-dimensional data, as facial images typically contain a large number of pixels. To address this issue, dimensionality reduction techniques such as Linear Discriminant Analysis (LDA) can be used. In face recognition, LDA can be used to extract discriminative features from facial images and reduce the dimensionality of the feature space, thereby improving the recognition system’s performance. This proposed study aims to conduct a comparative study between class-dependent and class-independent algorithms of LDA for face recognition. both algorithms were applied to the ORL dataset containing 400 images within 40 classes and compared the results were by applying SVM as a classification model.