Advantages of 3D methods for face recognition research in humans

This source preferred by Changhong Liu

This data was imported from Scopus:

Authors: Liu, C.H. and Ward, J.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 3723 LNCS

Pages: 244-254

eISSN: 1611-3349

ISSN: 0302-9743

Research on face recognition in humans has mainly relied on 2D images. This approach has certain limitations. First, observers become relatively passive in face encoding, although in reality they may be more spontaneous in exploring different views of a 3D face. Moreover, the volumetric information of a face is often confined to pictorial depth cues, making it difficult to assess the role of 3D shape processing. This paper demonstrates that 1) actively exploring different views of 3D face models produces more robust recognition memory than passively viewing playback of the same moving stimuli, 2) face matching across 2D and 3D representations typically incurs a cost, which alludes to depth-cue dependent processes in face recognition, and 3) combining multiple depth cues such as stereopsis and perspective can facilitate recognition performance even though a single depth cue alone rarely produces measurable benefits. © Springer-Verlag Berlin Heidelberg 2005.

This data was imported from Web of Science (Lite):

Authors: Liu, C.H. and Ward, J.

Journal: ANALYSIS AND MODELLING OF FACES AND GESTURES, PROCEEDINGS

Volume: 3723

Pages: 244-254

ISSN: 0302-9743

The data on this page was last updated at 04:53 on December 15, 2018.