A low-rank tensor-based algorithm for face recognition

The face recognition problem arises in a wide range of real life applications. Our new developed face recognition algorithm, based on higher order singular value decomposition (HOSVD) makes use of only third order tensor. A convenient way of writing the commutativity of different modes of tensor-matrix multiplications leads to a new method that outperforms in terms of complexity another third order tensor method. The resulting algorithm is more successful (in terms of recognition rate) than the conventional eigenfaces algorithm. Its effectiveness is proved for two benchmark datasets (ExtYaleB and Essex datasets), which are ensembles of facial images that combine different modes, like facial geometries, illuminations, and expressions.