Face recognition using ortho-diffusion bases

This paper proposes a new approach for face recognition by representing inter-face variation using orthogonal decompositions with embedded diffusion. The modified Gram-Schmidt with pivoting the columns orthogonal decomposition, called also QR algorithm, is applied recursively to the covariance matrix of a set of images forming the training set. At each recursion a set of orthonormal bases functions are extracted for a specific scale. A diffusion step is embedded at each scale in the QR decomposition. The algorithm models the main variations of face features from the training set by preserving only the most significant bases while eliminating noise and non-essential features. Each face is represented by a weighted sum of such representative bases functions, called ortho-diffusion faces.