View specific generalisation effects in face recognition: Front and yaw comparison views are better than pitch.

It can be difficult to recognise new instances of an unfamiliar face. Recognition errors in this particular situation appear to be viewpoint dependent with error rates increasing with the angular distance between the face views. Studies using front views for comparison have shown that recognising fa...

Full description

Bibliographic Details
Main Authors: Simone Favelle, Stephen Palmisano
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2018-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0209927
Description
Summary:It can be difficult to recognise new instances of an unfamiliar face. Recognition errors in this particular situation appear to be viewpoint dependent with error rates increasing with the angular distance between the face views. Studies using front views for comparison have shown that recognising faces rotated in yaw can be difficult and that recognition of faces rotated in pitch is more challenging still. Here we investigate the extent to which viewpoint dependent face recognition depends on the comparison view. Participants were assigned to one of four different comparison view groups: front, ¾ yaw (right), ¾ pitch-up (above) or ¾ pitch-down (below). On each trial, participants matched their particular comparison view to a range of yaw or pitch rotated test views. Results showed that groups with a front or ¾ yaw comparison view had superior overall performance and more successful generalisation to a broader range of both pitch and yaw test views compared to groups with pitch-up or pitch-down comparison views, both of which had a very restricted generalisation range. Regression analyses revealed the importance of image similarity between views for generalisation, with a lesser role for 3D face depth. These findings are consistent with a view interpolation solution to view generalisation of face recognition, with front and ¾ yaw views being most informative.
ISSN:1932-6203