Multi-source deep transfer learning for cross-sensor biometrics

• Autor(a) principal: Chetak Kandaswamy
• Data de Publicação: 2017
• Outros Autores: Monteiro,JC, Silva,LM, Jaime Cardoso
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://repositorio.inesctec.pt/handle/123456789/6076; http://dx.doi.org/10.1007/s00521-016-2325-5

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.