Development of a Method for Identifying People by Processing Digital Images from Handprint

Victor A. Tuesta-Monteza, Barny N. Cespedes-Ordoñez, Heber I. Mejia-Cabrera, Manuel G. Forero

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)


Fingerprint recognition methods present problems due to the fact that some prints are blurred or have changes due to the activities carried out with the hands by some people. In addition, these identification methods can be violated by using false fingerprints or other devices. Therefore, it is necessary to develop more reliable methods. For this purpose, a handprint-based identification method is presented in this paper. A database was built with the right handprints of 100 construction workers. The method comprises an image pre-processing and a classification stage based on deep learning. Six neural networks were compared VGG16, VG19, ResNet50, MobileNetV2, Xception and DenseNet121. The best results were obtained with the RestNet50 network, achieving 99% accuracy, followed by Xception with 97%. Showing the reliability of the proposed technique.

Idioma originalInglés
Título de la publicación alojadaPattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings
EditoresEdgar Roman-Rangel, Ángel Fernando Kuri-Morales, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas9
ISBN (versión impresa)9783030770037
EstadoPublicada - 2021
Publicado de forma externa
Evento13th Mexican Conference on Pattern Recognition, MCPR 2021 - Virtual, Online
Duración: 23 jun. 202126 jun. 2021

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12725 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349


Conferencia13th Mexican Conference on Pattern Recognition, MCPR 2021
CiudadVirtual, Online

Nota bibliográfica

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


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