| dc.contributor.author |
Schyns, Philippe G. |
en_US |
| dc.contributor.author |
Bulthoff, Heinrich H. |
en_US |
| dc.date.accessioned |
2004-10-20T20:49:58Z |
|
| dc.date.available |
2004-10-20T20:49:58Z |
|
| dc.date.issued |
1993-08-01 |
en_US |
| dc.identifier.other |
AIM-1432 |
en_US |
| dc.identifier.other |
CBCL-081 |
en_US |
| dc.identifier.uri |
http://hdl.handle.net/1721.1/7213 |
|
| dc.description.abstract |
Poggio and Vetter (1992) showed that learning one view of a bilaterally symmetric object could be sufficient for its recognition, if this view allows the computation of a symmetric, "virtual," view. Faces are roughly bilaterally symmetric objects. Learning a side-view--which always has a symmetric view--should allow for better generalization performances than learning the frontal view. Two psychophysical experiments tested these predictions. Stimuli were views of shaded 3D models of laser-scanned faces. The first experiment tested whether a particular view of a face was canonical. The second experiment tested which single views of a face give rise to best generalization performances. The results were compatible with the symmetry hypothesis: Learning a side view allowed better generalization performances than learning the frontal view. |
en_US |
| dc.description.provenance |
Made available in DSpace on 2004-10-20T20:49:58Z (GMT). No. of bitstreams: 2
AIM-1432.ps.Z: 215801 bytes, checksum: 190c799b9c64f6e9e6d6785553ee0f48 (MD5)
AIM-1432.pdf: 746385 bytes, checksum: 74ecf2806c07a47af21d4e1edf5ec22c (MD5)
Previous issue date: 1993-08-01 |
en |
| dc.format.extent |
6 p. |
en_US |
| dc.format.extent |
215801 bytes |
|
| dc.format.extent |
746385 bytes |
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| dc.format.mimetype |
application/octet-stream |
|
| dc.format.mimetype |
application/pdf |
|
| dc.language.iso |
en_US |
|
| dc.relation.ispartofseries |
AIM-1432 |
en_US |
| dc.relation.ispartofseries |
CBCL-081 |
en_US |
| dc.subject |
face recognition |
en_US |
| dc.subject |
RBF Network Symmetry |
en_US |
| dc.title |
Conditions for Viewpoint Dependent Face Recognition |
en_US |
