Published

2024-06-27

Clasificación de criptogramas faciales a través de sus características de textura local

Facial Cryptograms Classification through their Local Texture Features

DOI:

https://doi.org/10.15446/ing.investig.106069

Keywords:

reconocimiento facial, eficiencia de la clasificación, caos, criptografía, representación de cluster coordinado, características texturales locales (es)
facial recognition, classification efficiency, chaos, cryptography, coordinated clusters representation, local textural features (en)

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Authors

  • Maricela Jiménez Rodríguez Universidad de Guadalajara http://orcid.org/0000-0002-4935-2731
  • José Trinidad Guillen Bonilla Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara
  • Jorge Aguilar Santiago Centro Universitario de la Ciénega, Universidad de Guadalajara https://orcid.org/0000-0002-3283-5569
  • Juan Carlos Estrada Gutiérrez Centro Universitario de la Ciénega, Universidad de Guadalajara https://orcid.org/0000-0002-6727-3500

Con el uso creciente de las redes sociales, personas no autorizadas han conseguido detectar o interceptar datos personales, que podrían utilizarse de manera inapropiada, causando así daños personales. Por lo tanto, es esencial utilizar un mecanismo de seguridad que ayude a proteger la información de ataques maliciosos. En este trabajo se propone el reconocimiento facial, utilizando las características texturales locales de los criptogramas. Se cifraron imágenes faciales en formato Red-Green-Blue (RGB) aplicando el modelo matemático de Mapa Logístico, lo que generó un criptograma. Las características texturales locales de estos criptogramas se extrajeron mediante la transformación de representación de cluster coordinado (CCR). La alta eficiencia de clasificación (97-100%) de las imágenes faciales cifradas fue validada experimentalmente utilizando dos bases de datos: la primera fue generada controlando parámetros como la rotación, escala e iluminación; y la segunda es una base de datos pública. Esta técnica es adecuada para una amplia gama de aplicaciones relacionadas con la autenticación de usuarios, y protege la identidad de los usuarios autorizados cuando se acompaña de capas adicionales de seguridad que involucran imágenes de interés, como las utilizadas en el campo médico, mejorando la seguridad de los usuarios cuyas enfermedades se estudian gráficamente en los hospitales. Además, esta técnica puede desplegarse para proteger lanzamientos de nuevos productos donde las imágenes son importantes, como ropa, calzado, mosaicos, etc., ya que no es necesario descifrar las imágenes para clasificarlas.

With the increasing use of social networks, unauthorized individuals have become able to detect or intercept personal data, which could be used improperly, thereby causing personal damage. Therefore, it is essential to utilize a security mechanism that aids in protecting information from malicious attacks. In this work, facial recognition is proposed, using the local textural features of cryptograms. Red-Green-Blue (RGB) facial images were encrypted by applying the mathematical Logistic Map model, which generated a cryptogram. These cryptogram’s local textural features were extracted via Coordinated Cluster Representation (CCR) transform. The high classification efficiency (97-100%) of the encrypted facial images was experimentally validated using two databases: the first one was generated by controlling parameters such as rotation, scale, and lighting; and the second one is a public database. This technique is suitable for a wide range of applications related to user authentication, and it safeguards the identity of authorized users when accompanied by additional layers of security involving images of interest, such as those employed by the medical field, enhancing the security of users whose diseases are graphically studied in hospitals. In addition, this technique can be deployed to protect new product launches where images are important, such as clothing, footwear, mosaics, etc., since one does not need to decrypt the images in order to classify them.

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How to Cite

APA

Jiménez Rodríguez, M. ., Guillen Bonilla, J. T., Aguilar Santiago, J. and Estrada Gutiérrez, J. C. (2024). Clasificación de criptogramas faciales a través de sus características de textura local. Ingeniería e Investigación, 44(2), e106069. https://doi.org/10.15446/ing.investig.106069

ACM

[1]
Jiménez Rodríguez, M. , Guillen Bonilla, J.T., Aguilar Santiago, J. and Estrada Gutiérrez, J.C. 2024. Clasificación de criptogramas faciales a través de sus características de textura local. Ingeniería e Investigación. 44, 2 (Feb. 2024), e106069. DOI:https://doi.org/10.15446/ing.investig.106069.

ACS

(1)
Jiménez Rodríguez, M. .; Guillen Bonilla, J. T.; Aguilar Santiago, J.; Estrada Gutiérrez, J. C. Clasificación de criptogramas faciales a través de sus características de textura local. Ing. Inv. 2024, 44, e106069.

ABNT

JIMÉNEZ RODRÍGUEZ, M. .; GUILLEN BONILLA, J. T.; AGUILAR SANTIAGO, J.; ESTRADA GUTIÉRREZ, J. C. Clasificación de criptogramas faciales a través de sus características de textura local. Ingeniería e Investigación, [S. l.], v. 44, n. 2, p. e106069, 2024. DOI: 10.15446/ing.investig.106069. Disponível em: https://revistas.unal.edu.co/index.php/ingeinv/article/view/106069. Acesso em: 3 feb. 2025.

Chicago

Jiménez Rodríguez, Maricela, José Trinidad Guillen Bonilla, Jorge Aguilar Santiago, and Juan Carlos Estrada Gutiérrez. 2024. “Clasificación de criptogramas faciales a través de sus características de textura local”. Ingeniería E Investigación 44 (2):e106069. https://doi.org/10.15446/ing.investig.106069.

Harvard

Jiménez Rodríguez, M. ., Guillen Bonilla, J. T., Aguilar Santiago, J. and Estrada Gutiérrez, J. C. (2024) “Clasificación de criptogramas faciales a través de sus características de textura local”, Ingeniería e Investigación, 44(2), p. e106069. doi: 10.15446/ing.investig.106069.

IEEE

[1]
M. . Jiménez Rodríguez, J. T. Guillen Bonilla, J. Aguilar Santiago, and J. C. Estrada Gutiérrez, “Clasificación de criptogramas faciales a través de sus características de textura local”, Ing. Inv., vol. 44, no. 2, p. e106069, Feb. 2024.

MLA

Jiménez Rodríguez, M. ., J. T. Guillen Bonilla, J. Aguilar Santiago, and J. C. Estrada Gutiérrez. “Clasificación de criptogramas faciales a través de sus características de textura local”. Ingeniería e Investigación, vol. 44, no. 2, Feb. 2024, p. e106069, doi:10.15446/ing.investig.106069.

Turabian

Jiménez Rodríguez, Maricela, José Trinidad Guillen Bonilla, Jorge Aguilar Santiago, and Juan Carlos Estrada Gutiérrez. “Clasificación de criptogramas faciales a través de sus características de textura local”. Ingeniería e Investigación 44, no. 2 (February 20, 2024): e106069. Accessed February 3, 2025. https://revistas.unal.edu.co/index.php/ingeinv/article/view/106069.

Vancouver

1.
Jiménez Rodríguez M, Guillen Bonilla JT, Aguilar Santiago J, Estrada Gutiérrez JC. Clasificación de criptogramas faciales a través de sus características de textura local. Ing. Inv. [Internet]. 2024 Feb. 20 [cited 2025 Feb. 3];44(2):e106069. Available from: https://revistas.unal.edu.co/index.php/ingeinv/article/view/106069

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