Face and Object Recognition Using Biological Features and Few Views

Abstract

In recent years, a large number of impressive face and object recognition algorithms have surfaced, both computational and biologically inspired. Only a few of these can detect face and object views. Empirical studies concerning face and object recognition suggest that faces and objects may be stored in our memory by a few canonical representations. In cortical area V1 exist double-opponent colour blobs, also simple, complex, and end-stopped cells that provide input for a multiscale line and edge representation, keypoints for dynamic feature routing, and saliency maps for Focus-of-Attention. All these combined allow us to segregate faces. Events of different facial views are stored in memory and combined in order to identify the view and recognise a face, including its expression. The authors show that with five 2D views and their cortical representations it is possible to determine the left-right and frontal-lateral-profile views, achieving view-invariant recognition. They also show that the same principle with eight views can be applied to 3D object recognition when they are mainly rotated about the vertical axis. Although object recognition is here explored as a special case of face recognition, it should be stressed that faces and general objects are processed in different ways in the cortex.