In this paper, we proposed a novel classification method for face recognition which adopts the functionalities of linear discriminant and regression. Linear discriminant and regression analysis methods have benefits regarding minimising time, memory usage and better feature extraction. Linear regression and discriminant classification (LRDC) makes use of the principle that a sample class lie in a linear subspace, proposed method represents a predicted image as a linear combination of class-specific galleries. LRDC belongs to the category of nearest subspace classification and finds the set of optimal discriminant projection vectors by adopting singular value decomposition (SVD) and null space, and the decision made for a class with the minimum distance. LRDC is extensively evaluated by applying it to different classified datasets and compared with the state-of-the-art algorithms.