Multi-source deep transfer learning for cross-sensor biometrics Chetak Kandaswamy en Monteiro,JC en Silva,LM en Jaime Cardoso en 2018-01-14T20:45:36Z 2018-01-14T20:45:36Z 2017 en
dc.description.abstract Deep transfer learning emerged as a new paradigm in machine learning in which a deep model is trained on a source task and the knowledge acquired is then totally or partially transferred to help in solving a target task. In this paper, we apply the source-target-source methodology, both in its original form and an extended multi-source version, to the problem of cross-sensor biometric recognition. We tested the proposed methodology on the publicly available CSIP image database, achieving state-of-the-art results in a wide variety of cross-sensor scenarios. en
dc.identifier.uri en
dc.language eng en
dc.relation 3889 en
dc.relation 5808 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Multi-source deep transfer learning for cross-sensor biometrics en
dc.type article en
dc.type Publication en
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