Ortho-diffusion decompositions for face recognition from low quality images

We propose a new approach for recognizing human from images of low quality. An ortho-diffusion decomposition is used on graph representations of images. This is implemented by a recursive algorithm in three steps on either the covariance matrix or on the correlation of the training set. The first stage consists of an orthonormal decomposition implemented through the modified Gram-Schmidt with pivoting the columns. The other two stages consists of the data reduction and diffusion on graph representations. The data reduction ensures that the most significant features are preserved and together with the diffusion step ensures robustness to a variety of data corruption factors. The proposed methodology produces a set of ortho-diffusion bases representing the quintessential information from the training data set. The resulting orhto-diffusion bases are used to model face images when considering low resolution and corruption by various noise distributions.