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Title: Multi-source deep transfer learning for cross-sensor biometrics
Authors: Chetak Kandaswamy
Jaime Cardoso
Issue Date: 2017
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.
metadata.dc.type: article
Appears in Collections:CRAS - Articles in International Journals
CTM - Articles in International Journals

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