Don't You Forget About Me: A Study on Long-Term Performance in ECG Biometrics

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Gabriel Lopes (Autor) Outra; João Ribeiro Pinto (Autor) Outra; Jaime S. Cardoso (Autor) FEUP

Ata de Conferência Internacional

Título: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Páginas: 38-49

9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019

1 July 2019 through 4 July 2019

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00R-2EP

DOI: 10.1007/978-3-030-31321-0_4

Abstract (EN): The performance of biometric systems is known to decay over time, eventually rendering them ineffective. Focused on ECG-based biometrics, this work aims to study the permanence of these signals for biometric identification in state-of-the-art methods, and measure the effect of template update on their long-term performance. Ensuring realistic testing settings, four literature methods based on autocorrelation, autoencoders, and discrete wavelet and cosine transforms, were evaluated with and without template update, using Holter signals from THEW¿s E-HOL 24 h database. The results reveal ECG signals are unreliable for long-term biometric applications, and template update techniques offer considerable improvements over the state-of-the-art results. Nevertheless, further efforts are required to ensure long-term effectiveness in real applications. © 2019, Springer Nature Switzerland AG.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12