A Novel Approach to Combining Client-Dependent and Confidence Information in Multimodal Biometrics. Poh, N. & Bengio, S. In Kanade, T., Jain, A., & Ratha, N. K., editors, 5th International Conference on Audio- and Video-Based Biometric Person Authentication, AVBPA, Lecture Notes in Computer Science, volume LNCS 3546, pages 1120–1129, 2005. Springer-Verlag. ![A Novel Approach to Combining Client-Dependent and Confidence Information in Multimodal Biometrics [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex The issues of fusion with client-dependent and confidence information have been well studied separately in biometric authentication. In this study, we propose to take advantage of both sources of information in a discriminative framework. Initially, each source of information is processed on a per expert basis (plus on a per client basis for the first information and on a per example basis for the second information). Then, both sources of information are combined using a second-level classifier, across different experts. Although the formulation of such two-step solution is not new, the novelty lies in the way the sources of prior knowledge are incorporated prior to fusion using the second-level classifier. Because these two sources of information are of very different nature, one often needs to devise special algorithms to combine both information sources. Our framework that we call ``Prior Knowledge Incorporation'' has the advantage of using the standard machine learning algorithms. Based on 10 times 32=320 intramodal and multimodal fusion experiments carried out on the publicly available XM2VTS score-level fusion benchmark database, it is found that the generalisation performance of combining both information sources improves over using either or none of them, thus achieving a new state-of-the-art performance on this database.
@inproceedings{poh:2005:avbpa:combining,
author = {N. Poh and S. Bengio},
title = {A Novel Approach to Combining Client-Dependent and Confidence Information in Multimodal Biometrics},
booktitle = {5th International Conference on Audio- and Video-Based Biometric Person Authentication, {AVBPA}, Lecture Notes in Computer Science},
editor = {T. Kanade and A. Jain and N. K. Ratha},
publisher = {Springer-Verlag},
volume = {LNCS 3546},
pages = {1120--1129},
year = 2005,
url = {publications/ps/poh_2005_avbpa_combining.ps.gz},
pdf = {publications/pdf/poh_2005_avbpa_combining.pdf},
djvu = {publications/djvu/poh_2005_avbpa_combining.djvu},
idiap = {publications/pdf/rr04-68.pdf},
original = {2005/fusion_combining_avbpa},
topics = {biometric_authentication},
web = {http://dx.doi.org/10.1007/11527923_116},
abstract = {The issues of fusion with client-dependent and confidence information have been well studied separately in biometric authentication. In this study, we propose to take advantage of both sources of information in a discriminative framework. Initially, each source of information is processed on a per expert basis (plus on a per client basis for the first information and on a per example basis for the second information). Then, both sources of information are combined using a second-level classifier, across different experts. Although the formulation of such two-step solution is not new, the novelty lies in the way the sources of prior knowledge are incorporated prior to fusion using the second-level classifier. Because these two sources of information are of very different nature, one often needs to devise special algorithms to combine both information sources. Our framework that we call ``Prior Knowledge Incorporation'' has the advantage of using the standard machine learning algorithms. Based on 10 times 32=320 intramodal and multimodal fusion experiments carried out on the publicly available XM2VTS score-level fusion benchmark database, it is found that the generalisation performance of combining both information sources improves over using either or none of them, thus achieving a new state-of-the-art performance on this database.},
categorie = {C},
}
Downloads: 0
{"_id":"Ca4YvwtJFWFPZM4Kq","bibbaseid":"poh-bengio-anovelapproachtocombiningclientdependentandconfidenceinformationinmultimodalbiometrics-2005","authorIDs":[],"author_short":["Poh, N.","Bengio, S."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["N."],"propositions":[],"lastnames":["Poh"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Bengio"],"suffixes":[]}],"title":"A Novel Approach to Combining Client-Dependent and Confidence Information in Multimodal Biometrics","booktitle":"5th International Conference on Audio- and Video-Based Biometric Person Authentication, AVBPA, Lecture Notes in Computer Science","editor":[{"firstnames":["T."],"propositions":[],"lastnames":["Kanade"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Jain"],"suffixes":[]},{"firstnames":["N.","K."],"propositions":[],"lastnames":["Ratha"],"suffixes":[]}],"publisher":"Springer-Verlag","volume":"LNCS 3546","pages":"1120–1129","year":"2005","url":"publications/ps/poh_2005_avbpa_combining.ps.gz","pdf":"publications/pdf/poh_2005_avbpa_combining.pdf","djvu":"publications/djvu/poh_2005_avbpa_combining.djvu","idiap":"publications/pdf/rr04-68.pdf","original":"2005/fusion_combining_avbpa","topics":"biometric_authentication","web":"http://dx.doi.org/10.1007/11527923_116","abstract":"The issues of fusion with client-dependent and confidence information have been well studied separately in biometric authentication. In this study, we propose to take advantage of both sources of information in a discriminative framework. Initially, each source of information is processed on a per expert basis (plus on a per client basis for the first information and on a per example basis for the second information). Then, both sources of information are combined using a second-level classifier, across different experts. Although the formulation of such two-step solution is not new, the novelty lies in the way the sources of prior knowledge are incorporated prior to fusion using the second-level classifier. Because these two sources of information are of very different nature, one often needs to devise special algorithms to combine both information sources. Our framework that we call ``Prior Knowledge Incorporation'' has the advantage of using the standard machine learning algorithms. Based on 10 times 32=320 intramodal and multimodal fusion experiments carried out on the publicly available XM2VTS score-level fusion benchmark database, it is found that the generalisation performance of combining both information sources improves over using either or none of them, thus achieving a new state-of-the-art performance on this database.","categorie":"C","bibtex":"@inproceedings{poh:2005:avbpa:combining,\n author = {N. Poh and S. Bengio},\n title = {A Novel Approach to Combining Client-Dependent and Confidence Information in Multimodal Biometrics},\n booktitle = {5th International Conference on Audio- and Video-Based Biometric Person Authentication, {AVBPA}, Lecture Notes in Computer Science},\n editor = {T. Kanade and A. Jain and N. K. Ratha},\n publisher = {Springer-Verlag},\n volume = {LNCS 3546},\n pages = {1120--1129},\n year = 2005,\n url = {publications/ps/poh_2005_avbpa_combining.ps.gz},\n pdf = {publications/pdf/poh_2005_avbpa_combining.pdf},\n djvu = {publications/djvu/poh_2005_avbpa_combining.djvu},\n idiap = {publications/pdf/rr04-68.pdf},\n original = {2005/fusion_combining_avbpa},\n topics = {biometric_authentication},\n web = {http://dx.doi.org/10.1007/11527923_116},\n abstract = {The issues of fusion with client-dependent and confidence information have been well studied separately in biometric authentication. In this study, we propose to take advantage of both sources of information in a discriminative framework. Initially, each source of information is processed on a per expert basis (plus on a per client basis for the first information and on a per example basis for the second information). Then, both sources of information are combined using a second-level classifier, across different experts. Although the formulation of such two-step solution is not new, the novelty lies in the way the sources of prior knowledge are incorporated prior to fusion using the second-level classifier. Because these two sources of information are of very different nature, one often needs to devise special algorithms to combine both information sources. Our framework that we call ``Prior Knowledge Incorporation'' has the advantage of using the standard machine learning algorithms. Based on 10 times 32=320 intramodal and multimodal fusion experiments carried out on the publicly available XM2VTS score-level fusion benchmark database, it is found that the generalisation performance of combining both information sources improves over using either or none of them, thus achieving a new state-of-the-art performance on this database.},\n categorie = {C},\n}\n\n","author_short":["Poh, N.","Bengio, S."],"editor_short":["Kanade, T.","Jain, A.","Ratha, N. K."],"key":"poh:2005:avbpa:combining","id":"poh:2005:avbpa:combining","bibbaseid":"poh-bengio-anovelapproachtocombiningclientdependentandconfidenceinformationinmultimodalbiometrics-2005","role":"author","urls":{"Paper":"http://bengio.abracadoudou.com/publications/ps/poh_2005_avbpa_combining.ps.gz"},"downloads":0},"bibtype":"inproceedings","biburl":"http://bengio.abracadoudou.com/samy.bib","creationDate":"2020-03-18T03:43:27.397Z","downloads":0,"keywords":[],"search_terms":["novel","approach","combining","client","dependent","confidence","information","multimodal","biometrics","poh","bengio"],"title":"A Novel Approach to Combining Client-Dependent and Confidence Information in Multimodal Biometrics","year":2005,"dataSources":["9NCW2CDr4M3s5DvNX"]}