Discriminant Non-negative Matrix Factorization and projected gradients for frontal face verification

A novel Discriminant Non-negative Matrix Factorization (DNMF) method that uses projected gradients, is presented in this paper. The proposed algorithm guarantees the algorithm's convergence to a stationary point, contrary to the methods introduced so far, that only ensure the non-increasing behavior of the algorithm's cost function. The proposed algorithm employs some extra modifications that make the method more suitable for classification tasks. The usefulness of the proposed technique to the frontal face verification problem is also demonstrated.