Latent space visualization of half face and full face by generative model

Abstract

Generally, most face detection and recognition tasks are based on the training of intact facial images and their corresponding labels. The training image is supposed to contain as much facial area as possible, and sometimes expanding the training image area to the upper body may also enhance the learning ability. However, we noticed that both the three-dimensional structure and two-dimensional appearance from the frontal view of human faces are bilaterally symmetrical. Few research makes use of this characteristics to simplify the learning process. We have proposed a flipping strategy to apply the facial symmetrical characteristic to transfer learning and proved training with half faces can also achieve equivalent performance in face recognition for a small group of individuals. This paper extend the transfer learning of cropped half face images for face recognition rather than flipping the half face. The facial symmetrical characteristics is utilized to improve face recognition through transfer learning of only a half of the common human face image. We also investigate and explain the reason why the half face area is enough to accurately classify small groups of individuals. A variational autoencoder network is utilized to impose the probability distribution on the facial latent space. Finally, the dimensions of the facial latent space are reduced to visualize the distributed perceptual manifold for face identity.