Multi-source deep transfer learning for cross-sensor biometrics

• Type: Article in International Scientific Journal
• Date: 2017

Authors

Kandaswamy, C (Author) Other; Monteiro, JC (Author) Other; Silva, LM (Author) FEUP; Jaime S Cardoso (Author) FEUP

Journal

Title: Neural Computing and Applications

Vol. 28

Pages: 2461-2475

ISSN: 0941-0643

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 13 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15