Multi-source deep transfer learning for cross-sensor biometrics

• Tipo: Artigo em Revista Científica Internacional
• Data: 2017

Autores

Kandaswamy, C (Autor) Outra; Monteiro, JC (Autor) Outra; Silva, LM (Autor) FEUP; Jaime S Cardoso (Autor) FEUP

Revista

Título: Neural Computing and Applications

Vol. 28

Páginas: 2461-2475

ISSN: 0941-0643

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 9 Citações

Scopus - 13 Citações

Outras Informações

ID Authenticus: P-00M-T69

DOI: 10.1007/s00521-016-2325-5

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15