%Aigaion2 BibTeX export from Idiap Publications %Monday 09 December 2024 09:31:00 PM @INPROCEEDINGS{marcel:2002:cost, author = {Marcel, S{\'{e}}bastien and Marcel, Christine and Bengio, Samy}, projects = {Idiap}, title = {A State-of-the-art Neural Network for Robust Face Verification}, booktitle = {Proceedings of the COST275 Workshop on The Advent of Biometrics on the Internet}, year = {2002}, address = {Rome, Italy}, crossref = {marcel02-36irr}, pdf = {https://publications.idiap.ch/attachments/reports/2002/marcel_2002_cost.pdf}, postscript = {ftp://ftp.idiap.ch/pub/reports/2002/marcel_2002_cost.ps.gz}, ipdmembership={vision}, } crossreferenced publications: @TECHREPORT{Marcel02-36IRR, author = {Marcel, S{\'{e}}bastien and Marcel, Christine and Bengio, Samy}, projects = {Idiap}, title = {A State-of-the-art Neural Network for Robust Face Verification}, type = {Idiap-RR}, number = {Idiap-RR-36-2002}, year = {2002}, institution = {IDIAP}, note = {Published in the Proceedings of the COST275 Workshop on The Advent of Biometrics on the Internet, Rome, Italy, 7-8 November, 2002}, abstract = {The performance of face verification systems has steadily improved over the last few years, mainly focusing on models rather than on feature processing. State-of-the-art methods often use the gray-scale face image as input. In this paper, we propose to use an additional feature to the face image: the skin color. The new feature set is tested on a benchmark database, namely XM2VTS, using a simple discriminant artificial neural network. Results show that the skin color information improves the performance and that the proposed model achieves robust state-of-the-art results.}, pdf = {https://publications.idiap.ch/attachments/reports/2002/rr02-36.pdf}, postscript = {ftp://ftp.idiap.ch/pub/reports/2002/rr02-36.ps.gz}, ipdinar={2002}, ipdmembership={learning, vision}, language={English}, }