Modeling the effect of motion at encoding and retrieval for same and other race face recognition

Authors:
Hui Fang;Nicholas Costen;Natalie Butcher;Karen Lander
Affiliations:
School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, U.K.;School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, U.K.;School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, U.K.;School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, U.K.
Venue:
COST'11 Proceedings of the 2011 international conference on Cognitive Behavioural Systems
Year:
2011

Citing 4
Cited 0

Active Appearance Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information

Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Simultaneous Nonrigid Registration of Multiple Point Sets and Atlas Construction

IEEE Transactions on Pattern Analysis and Machine Intelligence
The painful face - Pain expression recognition using active appearance models

Image and Vision Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We assess the role of motion when encoding and recognizing unfamiliar faces, using a recognition memory paradigm. This reveals a facilitative role for non-rigid motion when learning unfamiliar same and other-race faces, and indicate that it is more important that the face is learned, rather than recognized, in motion. A computational study of the faces using Appearance Models of facial variation, shows that this lack a motion effect at recognition was reproduced by a norm-based encoding of faces, with the selection of features based on distance from the norm.