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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


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.

Original languageEnglish
Title of host publicationPattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings
EditorsEdgar Roman-Rangel, Ángel Fernando Kuri-Morales, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
ISBN (Print)9783030770037
StatePublished - 2021
Externally publishedYes
Event13th Mexican Conference on Pattern Recognition, MCPR 2021 - Virtual, Online
Duration: 23 Jun 202126 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12725 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th Mexican Conference on Pattern Recognition, MCPR 2021
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Biometrics
  • Convolutional Neural Networks
  • Hand print
  • Palmprint
  • ResNEt50
  • Security system
  • Xception


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