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

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

1 Cita (Scopus)

Resumen

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 original	Inglés
Título de la publicación alojada	Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings
Editores	Edgar Roman-Rangel, Ángel Fernando Kuri-Morales, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	231-239
Número de páginas	9
ISBN (versión impresa)	9783030770037
DOI	https://doi.org/10.1007/978-3-030-77004-4_22
Estado	Publicada - 2021
Publicado de forma externa	Sí
Evento	13th Mexican Conference on Pattern Recognition, MCPR 2021 - Virtual, Online Duración: 23 jun. 2021 → 26 jun. 2021

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	12725 LNCS
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conferencia

Conferencia	13th Mexican Conference on Pattern Recognition, MCPR 2021
Ciudad	Virtual, Online
Período	23/06/21 → 26/06/21

Nota bibliográfica

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

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

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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. En 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)).

@inproceedings{261af6377d3e45238db7a7b159634889,

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.}",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 13th Mexican Conference on Pattern Recognition, MCPR 2021 ; Conference date: 23-06-2021 Through 26-06-2021",

year = "2021",

doi = "10.1007/978-3-030-77004-4_22",

language = "Ingl{\'e}s",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "231--239",

editor = "Edgar Roman-Rangel and Kuri-Morales, {{\'A}ngel Fernando} and { Mart{\'i}nez-Trinidad}, {Jos{\'e} Francisco} and Carrasco-Ochoa, {Jes{\'u}s Ariel} and { Olvera-L{\'o}pez}, {Jos{\'e} Arturo}",

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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. En 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Development of a Method for Identifying People by Processing Digital Images from Handprint

AU - Tuesta-Monteza, Victor A.

AU - Cespedes-Ordoñez, Barny N.

AU - Mejia-Cabrera, Heber I.

AU - Forero, Manuel G.

PY - 2021

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.

KW - Biometrics

KW - Convolutional Neural Networks

KW - Hand print

KW - Palmprint

KW - ResNEt50

KW - Security system

KW - Xception

UR - http://www.scopus.com/inward/record.url?scp=85111376006&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-77004-4_22

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

M3 - Contribución a la conferencia

AN - SCOPUS:85111376006

SN - 9783030770037

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 231

EP - 239

BT - Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings

A2 - Roman-Rangel, Edgar

A2 - Kuri-Morales, Ángel Fernando

A2 - Martínez-Trinidad, José Francisco

A2 - Carrasco-Ochoa, Jesús Ariel

A2 - Olvera-López, José Arturo

PB - Springer Science and Business Media Deutschland GmbH

T2 - 13th Mexican Conference on Pattern Recognition, MCPR 2021

Y2 - 23 June 2021 through 26 June 2021

ER -

Tuesta-Monteza VA, Cespedes-Ordoñez BN, Mejia-Cabrera HI, Forero MG. Development of a Method for Identifying People by Processing Digital Images from Handprint. En Roman-Rangel E, Kuri-Morales ÁF, Martínez-Trinidad JF, Carrasco-Ochoa JA, Olvera-López JA, editores, 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

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

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