Abstract
Block linear discriminant analysis (LDA) is one of the face recognition methods suggested when only single image per person is available. However, the transformation in block LDA involves computation of inverse of within class scatter matrix, which may not exist when within class scatter matrix is singular. In order to overcome this, we present a novel technique called Block LDA via QR-Decomposition. The proposed technique does not involve the computation of within class scatter matrix. In addition, it is also computationally efficient and scalable. The performance of the proposed technique is compared with several other methods in terms of average classification accuracy and training time. The proposed technique possesses less computation complexity than several other methods and is suitable for real time applications. Experimental results on two publicly available datasets ORL and Yale demonstrate the efficacy of the proposed technique.