Face recognition of full-bodied avatars by active observers in a virtual environment

Viewing faces in motion or attached to a body instead of isolated static faces improves their subsequent recognition. Here we enhanced the ecological validity of face encoding by having observers physically moving in a virtual room populated by life-size avatars. We compared the recognition performance of this active group to two control groups. The first control group watched a passive reenactment of the visual experience of the active group. The second control group saw static screenshots of the avatars. All groups performed the same old/new recognition task after learning. Half of the learned faces were shown at test in an orientation close to that experienced during learning while the others were viewed from a new viewing angle. All observers found novel views more difficult to recognize than familiar ones. Overall, the active group performed better than both other groups. Furthermore, the group learning faces from static images was the only one to be at chance level in the novel-view condition. These findings suggest that active exploration combined with a dynamic experience of the faces to learn allow for more robust face recognition and point out the value of such techniques for integrating facial visual information and enhancing recognition from novel viewpoints.