DISENTANGLING PROTOTYPE AND VARIATION FOR SINGLE SAMPLE FACE RECOGNITION
Single sample per person face recognition (SSPP FR) is one of the most challenging problems in FR due to the extreme lack of enrolment data. State-of-the-art SSPP FR methods are based on the prototype plus variation (i.e., P+V) model. However, the classic P+V model has two major limitations: 1) It is a linear model and cannot generalize many non-linear variations; 2) It can be severely impaired once the enrolment face images are contaminated with variations. To this end, we propose a novel disentangled prototype plus variation model, dubbed DisP+V, to tackle such limitations. DisP+V consists of an encoder-decoder structural generator and two discriminators. The generator and discriminators play two adversarial games such that the generator nonlinearly encodes the images into a latent semantic space, where the more discriminative prototype feature and the less discriminative variation feature are disentangled. Meanwhile, the prototype and variation features in the latent space can guide the generator to generate an identity-preserved prototype and the corresponding variation, respectively. Experiments on various real-world face datasets demonstrate the superiority of our DisP+V model over the classic P+V model for SSPP FR. Furthermore, DisP+V demonstrates its unique characteristics in the challenging prototype recovery task.
