Journal
NEURAL COMPUTING & APPLICATIONS
Volume 28, Issue 9, Pages 2461-2475Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-016-2325-5
Keywords
Transfer learning; Deep neural networks; Source-target-source; Optimization; Cross-sensor biometrics
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Funding
- FEDER funds through the Programa Operacional Factores de Competitividade-COMPETE
- FCT-Fundacao para a Ciencia e Tecnologia [PTDC/EIA-EIA/119004/2010]
- Fundacao para a Ciencia e Tecnologia (FCT)-Portugal [SFRH/BD/87392/2012]
- Fundação para a Ciência e a Tecnologia [SFRH/BD/87392/2012, PTDC/EIA-EIA/119004/2010] Funding Source: FCT
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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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