Secure Triplet Loss for End-to-End Deep Biometrics

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2020

Autores

Joao Ribeiro Pinto (Autor) Outra; Jaime S. Cardoso (Autor) FEUP; Miguel V. Correia (Autor) FEUP

Ata de Conferência Internacional

Título: 8th International Workshop on Biometrics and Forensics, IWBF 2020, Porto, Portugal, April 29-30, 2020

Páginas: 1-6

8th International Workshop on Biometrics and Forensics, IWBF 2020

29 April 2020 through 30 April 2020

Indexação

ISI Web of Knowledge - 1 Citação

ISI Web of Science

Scopus - 2 Citações

INSPEC

Outras Informações

ID Authenticus: P-00S-6QV

DOI: 10.1109/iwbf49977.2020.9107958

Abstract (EN): Although deep learning is being widely adopted for every topic in pattern recognition, its use for secure and cancelable biometrics is currently reserved for feature extraction and biometric data preprocessing, limiting achievable performance. In this paper, we propose a novel formulation of the triplet loss methodology, designated as secure triplet loss, that enables biometric template cancelability with end-to-end convolutional neural networks, using easily changeable keys. Trained and evaluated for electrocardiogram-based biometrics, the network revealed easy to optimize using the modified triplet loss and achieved superior performance when compared with the state- of-the-art (10.63% equal error rate with data from 918 subjects of the UofTDB database). Additionally, it ensured biometric template security and effective template cancelability. Although further efforts are needed to avoid template linkability, the proposed secure triplet loss shows promise in template cancelability and non-invertibility for biometric recognition while taking advantage of the full power of convolutional neural networks. © 2020 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6