Face perception foundations for pattern recognition algorithms

Abstract

Pattern recognition system developers have looked in multiple directions over the years and designed a broad spectrum of methodologies for face identification and verification, both in 2D and 3D. These techniques rely on sound methods and experimentations, and currently give high to excellent recognition rates in terms of performance. Nonetheless, it seems that the most performing face recognition system, especially when familiar faces are involved, is still the human being, able to detect known faces in the wild, in presence of occlusions or extreme light contrast, caricatures, sketches, partial views, blurred images. This is one of the manifold reasons why the human visual system at eye and brain level and face perception techniques are currently being studied by neuroscientists and psychologists, with the aim to uncover the processes underneath the human vision.The purpose of this work is to review the current literature about perception foundations and related biologically-inspired methodologies for face recognition.