CONF
fasel02a-conf/IDIAP
Robust Face Analysis using Convolutional Neural Networks
Fasel, B.
EXTERNAL
https://publications.idiap.ch/attachments/reports/2001/rr01-48.pdf
PUBLIC
https://publications.idiap.ch/index.php/publications/showcite/fasel-rr-01-48
Related documents
Proceedings of the International Conference on Pattern Recognition (ICPR 02)
2
40-43
2002
Quebec, Canada
August 2002
IDIAP-RR 01-48
Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. We propose a data-driven face analysis approach that is not only capable of extracting features relevant to a given face analysis task, but is also robust with regard to face location changes and scale variations. This is achieved by deploying convolutional neural networks, which are either trained for facial expression recognition or face identity recognition. Combining the outputs of these networks allows us to obtain a subject dependent or personalized recognition of facial expressions.
REPORT
fasel-RR-01-48/IDIAP
Robust Face Analysis using Convolutional Neural Networks
Fasel, B.
EXTERNAL
https://publications.idiap.ch/attachments/reports/2001/rr01-48.pdf
PUBLIC
Idiap-RR-48-2001
2001
IDIAP
Published in the Proceedings of the International Conference on Pattern Recognition (ICPR 2002,',','),
Quebec, Canada, 2002
Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. We propose a data-driven face analysis approach that is not only capable of extracting features relevant to a given face analysis task, but is also robust with regard to face location changes and scale variations. This is achieved by deploying convolutional neural networks, which are either trained for facial expression recognition or face identity recognition. Combining the outputs of these networks allows us to obtain a subject dependent or personalized recognition of facial expressions.