An image of a face depends not only on its shape, but also on the viewpoint, illumination conditions, and facial expression. A face recognition system must overcome the changes in face appearance induced by these factors. Two related questions were investigated: the capacity of the human visual system to generalize the recognition of faces to novel images, and the level at which this generalization occurs. This problem was approached by comparing the identification and generalization capacity for upright and inverted faces. For upright faces, remarkably good generalization to novel conditions was found. For inverted faces, the generalization to novel views was significantly worse for both new illumination and viewpoint, although the performance on the training images was similar to that on the upright condition. The results indicate that at least some of the processes that support generalization across viewpoint and illumination are neither universal (because subjects did not generalize as easily for inverted faces as for upright ones) nor strictly object specific (because in upright faces nearly perfect generalization was possible from a single view, by itself insufficient for building a complete object-specific model). It is proposed that generalization in face recognition occurs at an intermediate level that is applicable to a class of objects, and that at this level upright and inverted faces initially constitute distinct object classes.
Integrate Receptive Field Block into Large-margin Softmax Loss for Face Recognition