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.
['Chetak Kandaswamy', 'João C. Monteiro', 'Luís M. Silva', 'Jaime S. Cardoso']
Multi-source deep transfer learning for cross-sensor biometrics