Feature selection for biometric recognition based on electrocardiogram signals (2017)
- Authors:
- USP affiliated authors: PERES, SARAJANE MARQUES - EACH ; LIMA, CLODOALDO APARECIDO DE MORAES - EACH
- Unidade: EACH
- DOI: 10.1109/IJCNN.2017.7966216
- Subjects: INTELIGÊNCIA ARTIFICIAL; BIOMETRIA; ELETROCARDIOGRAFIA
- Language: Inglês
- Imprenta:
- Publisher place: Piscataway
- Date published: 2017
- Source:
- Título: International Joint Conference on Neural Networks. Proceedings
- ISSN: 1098-7576
- Volume/Número/Paginação/Ano: v. 2017, p. 2911-2920, may/jun. 2017
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
TEODORO, Felipe Gustavo Silva e PERES, Sarajane Marques e LIMA, Clodoaldo Aparecido de Moraes. Feature selection for biometric recognition based on electrocardiogram signals. International Joint Conference on Neural Networks. Proceedings, v. 2017, n. may/ju 2017, p. 2911-2920, 2017Tradução . . Disponível em: https://doi.org/10.1109/IJCNN.2017.7966216. Acesso em: 10 fev. 2026. -
APA
Teodoro, F. G. S., Peres, S. M., & Lima, C. A. de M. (2017). Feature selection for biometric recognition based on electrocardiogram signals. International Joint Conference on Neural Networks. Proceedings, 2017( may/ju 2017), 2911-2920. doi:10.1109/IJCNN.2017.7966216 -
NLM
Teodoro FGS, Peres SM, Lima CA de M. Feature selection for biometric recognition based on electrocardiogram signals [Internet]. International Joint Conference on Neural Networks. Proceedings. 2017 ; 2017( may/ju 2017): 2911-2920.[citado 2026 fev. 10 ] Available from: https://doi.org/10.1109/IJCNN.2017.7966216 -
Vancouver
Teodoro FGS, Peres SM, Lima CA de M. Feature selection for biometric recognition based on electrocardiogram signals [Internet]. International Joint Conference on Neural Networks. Proceedings. 2017 ; 2017( may/ju 2017): 2911-2920.[citado 2026 fev. 10 ] Available from: https://doi.org/10.1109/IJCNN.2017.7966216 - Estudo comparativo de técnicas baseadas em subespaço na tarefa de reconstrução de faces parcialmente ocluídas
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Informações sobre o DOI: 10.1109/IJCNN.2017.7966216 (Fonte: oaDOI API)
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