Publicação

Dominant set approach to ECG biometrics

Detalhes bibliográficos
Resumo:	Electrocardiographic (ECG) signals are emerging as a recent trend in the field of biometrics. In this paper, we propose a novel ECG biometric system that combines clustering and classification methodologies. Our approach is based on dominant-set clustering, and provides a framework for outlier removal and template selection. It enhances the typical workflows, by making them better suited to new ECG acquisition paradigms that use fingers or hand palms, which lead to signals with lower signal to noise ratio, and more prone to noise artifacts. Preliminary results show the potential of the approach, helping to further validate the highly usable setups and ECG signals as a complementary biometric modality.
Autores principais:	Lourenço, André Ribeiro
Outros Autores:	Rota Bulò, Samuel; Carreiras, Carlos; Silva, Hugo; Fred, Ana L. N.; Pelillo, Marcello
Assunto:	Clustering Dominant Set Biometrics ECG Outlier Detection Template Selection
Ano:	2013
País:	Portugal
Tipo de documento:	capítulo de livro
Tipo de acesso:	acesso a metadados
Instituição associada:	Universidade Politécnica de Lisboa
Idioma:	inglês
Origem:	Repositório Científico da Universidade Politécnica de Lisboa

Descrição
Resumo:	Electrocardiographic (ECG) signals are emerging as a recent trend in the field of biometrics. In this paper, we propose a novel ECG biometric system that combines clustering and classification methodologies. Our approach is based on dominant-set clustering, and provides a framework for outlier removal and template selection. It enhances the typical workflows, by making them better suited to new ECG acquisition paradigms that use fingers or hand palms, which lead to signals with lower signal to noise ratio, and more prone to noise artifacts. Preliminary results show the potential of the approach, helping to further validate the highly usable setups and ECG signals as a complementary biometric modality.

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