Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Clustering Dominant Set Biometrics ECG Outlier Detection Template Selection
Contexto Educativo
Citação
LOURENÇO, André; BULÒ, Samuel Rota; CARREIRAS, Carlos; SILVA, Hugo; FRED, Ana L. N.; PELILLO, Marcello - Dominant Set Approach to ECG Biometrics. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Vol. 8258 (2013), p. 535-542.
Editora
Springer Berlin Heidelberg