Two dimensional incremental reduction PCA: A novel appearance based technique for image representation and recognition

In this paper, an efficient two dimensional incremental reduction principal component analysis approach (2DIRPCA) is developed for image representation and recognition. The 2DIRPCA technique computes the image covariance matrix of each image matrix as it arrives sequentially. Therefore, the contribution of each image to the projection matrices is added to the existing projection matrices. In this way, the 2DIRPCA method overcomes the limitations such as the computational cost and memory requirements making it suitable for real time applications. The feasibility of the proposed approach was tested on the ORL database consisting of 400 face images. The 2DIRPCA method shows superior performance in terms of computational time, storage and recognition accuracy (93.5%) with a 10 × 6 feature matrix compared to the 2DPCA (92.5%) with a 112 × 7 feature matrix.