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

doi:10.1007/978-3-030-77004-4_22

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 proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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 language	English
Title of host publication	Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings
Editors	Edgar Roman-Rangel, Ángel Fernando Kuri-Morales, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	231-239
Number of pages	9
ISBN (Print)	9783030770037
DOIs	https://doi.org/10.1007/978-3-030-77004-4_22
State	Published - 2021
Externally published	Yes
Event	13th Mexican Conference on Pattern Recognition, MCPR 2021 - Virtual, Online Duration: 23 Jun 2021 → 26 Jun 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12725 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th Mexican Conference on Pattern Recognition, MCPR 2021
City	Virtual, Online
Period	23/06/21 → 26/06/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Biometrics
Convolutional Neural Networks
Hand print
Palmprint
ResNEt50
Security system
Xception

Access to Document

10.1007/978-3-030-77004-4_22

Cite this

Tuesta-Monteza, V. A., Cespedes-Ordoñez, B. N., Mejia-Cabrera, H. I., & Forero, M. G. (2021). Development of a Method for Identifying People by Processing Digital Images from Handprint. In E. Roman-Rangel, Á. F. Kuri-Morales, J. F. Martínez-Trinidad, J. A. Carrasco-Ochoa, & J. A. Olvera-López (Eds.), Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings (pp. 231-239). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12725 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-77004-4_22

Tuesta-Monteza, Victor A. ; Cespedes-Ordoñez, Barny N. ; Mejia-Cabrera, Heber I. et al. / Development of a Method for Identifying People by Processing Digital Images from Handprint. Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings. editor / Edgar Roman-Rangel ; Ángel Fernando Kuri-Morales ; José Francisco Martínez-Trinidad ; Jesús Ariel Carrasco-Ochoa ; José Arturo Olvera-López. Springer Science and Business Media Deutschland GmbH, 2021. pp. 231-239 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Development of a Method for Identifying People by Processing Digital Images from Handprint",

abstract = "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.",

keywords = "Biometrics, Convolutional Neural Networks, Hand print, Palmprint, ResNEt50, Security system, Xception",

author = "Tuesta-Monteza, {Victor A.} and Cespedes-Ordo{\~n}ez, {Barny N.} and Mejia-Cabrera, {Heber I.} and Forero, {Manuel G.}",

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Tuesta-Monteza, VA, Cespedes-Ordoñez, BN, Mejia-Cabrera, HI & Forero, MG 2021, Development of a Method for Identifying People by Processing Digital Images from Handprint. in E Roman-Rangel, ÁF Kuri-Morales, JF Martínez-Trinidad, JA Carrasco-Ochoa & JA Olvera-López (eds), Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12725 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 231-239, 13th Mexican Conference on Pattern Recognition, MCPR 2021, Virtual, Online, 23/06/21. https://doi.org/10.1007/978-3-030-77004-4_22

Development of a Method for Identifying People by Processing Digital Images from Handprint. / Tuesta-Monteza, Victor A.; Cespedes-Ordoñez, Barny N.; Mejia-Cabrera, Heber I. et al.
Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings. ed. / Edgar Roman-Rangel; Ángel Fernando Kuri-Morales; José Francisco Martínez-Trinidad; Jesús Ariel Carrasco-Ochoa; José Arturo Olvera-López. Springer Science and Business Media Deutschland GmbH, 2021. p. 231-239 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12725 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Tuesta-Monteza, Victor A.

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AU - Forero, Manuel G.

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Y1 - 2021

N2 - 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.

AB - 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.

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Tuesta-Monteza VA, Cespedes-Ordoñez BN, Mejia-Cabrera HI, Forero MG. Development of a Method for Identifying People by Processing Digital Images from Handprint. In Roman-Rangel E, Kuri-Morales ÁF, Martínez-Trinidad JF, Carrasco-Ochoa JA, Olvera-López JA, editors, Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 231-239. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-77004-4_22