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.
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Acknowledgments
This work was financed by FEDER funds through the Programa Operacional Factores de Competitividade—COMPETE and by Portuguese funds through FCT—Fundação para a Ciência e Tecnologia in the framework of the project PTDC/EIA-EIA/119004/2010. The second author would like to thank Fundação para a Ciência e Tecnologia (FCT)—Portugal the financial support for the PhD grant SFRH/BD/87392/2012.
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Kandaswamy, C., Monteiro, J.C., Silva, L.M. et al. Multi-source deep transfer learning for cross-sensor biometrics. Neural Comput & Applic 28, 2461–2475 (2017). https://doi.org/10.1007/s00521-016-2325-5
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DOI: https://doi.org/10.1007/s00521-016-2325-5