Entropy-Based Face Recognition and Spoof Detection for Security Applications
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Título: | Entropy-Based Face Recognition and Spoof Detection for Security Applications |
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Autor/es: | Pujol, Francisco A. | Pujol López, María José | Rizo-Maestre, Carlos | Pujol, Mar |
Grupo/s de investigación o GITE: | UniCAD: Grupo de investigación en CAD/CAM/CAE de la Universidad de Alicante | Informática Industrial e Inteligencia Artificial | Tecnología y Sostenibilidad en Arquitectura |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Departamento de Matemática Aplicada | Universidad de Alicante. Departamento de Construcciones Arquitectónicas | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial |
Palabras clave: | Face recognition | Security | Spoofing | Histogram of oriented gradients | Smart cities |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores | Matemática Aplicada | Construcciones Arquitectónicas | Ciencia de la Computación e Inteligencia Artificial |
Fecha de publicación: | 20-dic-2019 |
Editor: | MDPI |
Cita bibliográfica: | Pujol FA, Pujol MJ, Rizo-Maestre C, Pujol M. Entropy-Based Face Recognition and Spoof Detection for Security Applications. Sustainability. 2020; 12(1):85. doi:10.3390/su12010085 |
Resumen: | Nowadays, cyber attacks are becoming an extremely serious issue, which is particularly important to prevent in a smart city context. Among cyber attacks, spoofing is an action that is increasingly common in many areas, such as emails, geolocation services or social networks. Identity spoofing is defined as the action by which a person impersonates a third party to carry out a series of illegal activities such as committing fraud, cyberbullying, sextorsion, etc. In this work, a face recognition system is proposed, with an application to the spoofing prevention. The method is based on the Histogram of Oriented Gradients (HOG) descriptor. Since different face regions do not have the same information for the recognition process, introducing entropy would quantify the importance of each face region in the descriptor. Therefore, entropy is added to increase the robustness of the algorithm. Regarding face recognition, our approach has been tested on three well-known databases (ORL, FERET and LFW) and the experiments show that adding entropy information improves the recognition rate significantly, with an increase over 40% in some of the considered databases. Spoofing tests has been implemented on CASIA FASD and MIFS databases, having obtained again better results than similar texture descriptors approaches. |
Patrocinador/es: | This work was partially supported by the Ministerio de Economía y Competitividad (Spain), project TIN2013-40982-R, the FEDER funds, and the “Red de Investigación en el uso del aprendizaje colaborativo para la adquisición de competencias básicas. El caso Erasmus+ EUROBOTIQUE”, Red ICE 3701 curso 2016–2017. |
URI: | http://hdl.handle.net/10045/101248 |
ISSN: | 2071-1050 |
DOI: | 10.3390/su12010085 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/su12010085 |
Aparece en las colecciones: | INV - TSA - Artículos de Revistas INV - i3a - Proyecto Erasmus+ EUROBOTIQUE INV - UNICAD - Artículos de Revistas INV - i3a - Artículos de Revistas |
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