Abstract
In this paper, we propose a computational architecture of face recognition based on evidence from cognitive research. Several recent psychophysical experiments have shown that humans process faces by a combination of configural and component information. Using an appearance-based implementation of this architecture based on low-level features and their spatial relations, we were able to model aspects of human performance found in psychophysical studies. Furthermore, results from additional computational recognition experiments show that our framework is able to achieve excellent recognition performance even under large view rotations. Our interdisciplinary study is an example of how results from cognitive research can be used to construct recognition systems with increased performance. Finally, our modeling results also make new experimental predictions that will be tested in further psychophysical studies, thus effectively closing the loop between psychophysical experimentation and computational modeling.
| Original language | English |
|---|---|
| Pages (from-to) | 401-418 |
| Number of pages | 18 |
| Journal | Network: Computation in Neural Systems |
| Volume | 16 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2005 Dec 1 |
| Externally published | Yes |
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Keywords
- Configural and component information
- Face recognition
- Local features
ASJC Scopus subject areas
- Neuroscience(all)
Cite this
Learning from humans : Computational modeling of face recognition. / Wallraven, Christian; Schwaninger, Adrian; Bulthoff, Heinrich.
In: Network: Computation in Neural Systems, Vol. 16, No. 4, 01.12.2005, p. 401-418.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Learning from humans
T2 - Computational modeling of face recognition
AU - Wallraven, Christian
AU - Schwaninger, Adrian
AU - Bulthoff, Heinrich
PY - 2005/12/1
Y1 - 2005/12/1
N2 - In this paper, we propose a computational architecture of face recognition based on evidence from cognitive research. Several recent psychophysical experiments have shown that humans process faces by a combination of configural and component information. Using an appearance-based implementation of this architecture based on low-level features and their spatial relations, we were able to model aspects of human performance found in psychophysical studies. Furthermore, results from additional computational recognition experiments show that our framework is able to achieve excellent recognition performance even under large view rotations. Our interdisciplinary study is an example of how results from cognitive research can be used to construct recognition systems with increased performance. Finally, our modeling results also make new experimental predictions that will be tested in further psychophysical studies, thus effectively closing the loop between psychophysical experimentation and computational modeling.
AB - In this paper, we propose a computational architecture of face recognition based on evidence from cognitive research. Several recent psychophysical experiments have shown that humans process faces by a combination of configural and component information. Using an appearance-based implementation of this architecture based on low-level features and their spatial relations, we were able to model aspects of human performance found in psychophysical studies. Furthermore, results from additional computational recognition experiments show that our framework is able to achieve excellent recognition performance even under large view rotations. Our interdisciplinary study is an example of how results from cognitive research can be used to construct recognition systems with increased performance. Finally, our modeling results also make new experimental predictions that will be tested in further psychophysical studies, thus effectively closing the loop between psychophysical experimentation and computational modeling.
KW - Configural and component information
KW - Face recognition
KW - Local features
UR - http://www.scopus.com/inward/record.url?scp=33646248423&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33646248423&partnerID=8YFLogxK
U2 - 10.1080/09548980500508844
DO - 10.1080/09548980500508844
M3 - Article
C2 - 16611592
AN - SCOPUS:33646248423
VL - 16
SP - 401
EP - 418
JO - Network: Computation in Neural Systems
JF - Network: Computation in Neural Systems
SN - 0954-898X
IS - 4
ER -