Abstract: Approaches based on the construction of highly discriminative models, such as one-against-all classification schemes, have been employed successfully in face identification. However, their main drawback is the reduction in the scalability once the models for each individual depend on the remaining subjects. Therefore, when new subjects are enrolled, it is necessary to rebuild all models to take into account the new individuals. This work addresses different classification schemes based on Partial Least Squares employed to face identification. First, the one-against-all and the one-against-some classification schemes are described and, based on their drawbacks, a classification scheme referred to as one-against-none is proposed. This novel approach considers face samples that do not belong to subjects in the gallery. Experimental results show that it achieves similar results to the one-against-all and one-against-some even though it does not depend on the remaining subjects in the gallery to build the models Ver menos