Paper Contents
Abstract
Deep learning models currently achieve human levels of performance on real-world face recognition tasks. We review scientific progress in understanding human face processing using computational approaches based on deep learning. This review is organized around three fundamental advances. First, deep networks trained for face identification generate a representation that retains structured information about the face (e.g., identity, demographics, appearance, social traits, expression) and the input image (e.g., viewpoint, illumination). Researchers have found that compared with other existing conditions (e.g., pleasantness), information relevant to survival produced a higher rate of retrieval; this effect is known as the survival processing advantage (SPA). Previous experiments have examined that the advantage of memory can be extended to some different types of visual pictorial material, such as pictures and short video clips, but there were some arguments for whether face stimulus could be seen as a boundary condition of SPA.
Copyright
Copyright © 2024 Mohammad Rafi. This is an open access article distributed under the Creative Commons Attribution License.
