Study of image sensors for enhanced face recognition at a distance in the Smart City context

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Title: Study of image sensors for enhanced face recognition at a distance in the Smart City context
Authors: Llauradó, José M. | Pujol, Francisco A. | Tomás, David | Visvizi, Anna | Pujol, Mar
Research Group/s: UniCAD: Grupo de investigación en CAD/CAM/CAE de la Universidad de Alicante | Procesamiento del Lenguaje y Sistemas de Información (GPLSI) | Informática Industrial e Inteligencia Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Image sensors | Deep learning | Face recognition | Smart Cities
Issue Date: 7-Sep-2023
Publisher: Springer Nature
Citation: Scientific Reports. 2023, 13:14713.
Abstract: Smart monitoring and surveillance systems have become one of the fundamental areas in the context of security applications in Smart Cities. In particular, video surveillance for Human Activity Recognition (HAR) applied to the recognition of potential offenders and to the detection and prevention of violent acts is a challenging task that is still undergoing. This paper presents a method based on deep learning for face recognition at a distance for security applications. Due to the absence of available datasets on face recognition at a distance, a methodology to generate a reliable dataset that relates the distance of the individuals from the camera, the focal length of the image sensors and the size in pixels of the target face is introduced. To generate the extended dataset, the Georgia Tech Face and Quality Dataset for Distance Faces databases were chosen. Our method is then tested and applied to a set of commercial image sensors for surveillance cameras using this dataset. The system achieves an average accuracy above 99% for several sensors and allows to calculate the maximum distance for a sensor to get the required accuracy in the recognition, which could be crucial in security applications in smart cities.
Sponsor: This work has been supported by the Ministerio de Ciencia, Innovación y Universidades (Spain), project RTI2018-096219-B-I00. Project co-financed with FEDER funds.
ISSN: 2045-2322
DOI: 10.1038/s41598-023-40110-y
Language: eng
Type: info:eu-repo/semantics/article
Rights: © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
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