dc.contributor | Escuela de Ingenierias Industrial, Informática y Aeroespacial | es_ES |
dc.contributor.author | Álvarez Aparicio, Claudia | |
dc.contributor.author | Guerrero Higueras, Ángel Manuel | |
dc.contributor.author | González Santamarta, Miguel Ángel | |
dc.contributor.author | Campazas Vega, Adrián | |
dc.contributor.author | Matellán Olivera, Vicente | |
dc.contributor.author | Fernández Llamas, Camino | |
dc.contributor.other | Arquitectura y Tecnologia de Computadores | es_ES |
dc.date | 2022-08-25 | |
dc.date.accessioned | 2024-02-07T13:57:54Z | |
dc.date.available | 2024-02-07T13:57:54Z | |
dc.identifier.citation | Álvarez-Aparicio, C., Guerrero-Higueras, Á. M., González-Santamarta, M. Á., Campazas-Vega, A., Matellán, V., & Fernández-Llamas, C. (2022). Biometric recognition through gait analysis. Scientific Reports, 12(1). https://doi.org/10.1038/S41598-022-18806-4 | es_ES |
dc.identifier.uri | https://hdl.handle.net/10612/18128 | |
dc.description.abstract | [EN] The use of people recognition techniques has become critical in some areas. For instance, social or assistive robots carry out collaborative tasks in the robotics field. A robot must know who to work with to deal with such tasks. Using biometric patterns may replace identification cards or codes on access control to critical infrastructures. The usage of Red Green Blue Depth (RGBD) cameras is ubiquitous to solve people recognition. However, this sensor has some constraints, such as they demand high computational capabilities, require the users to face the sensor, or do not regard users' privacy. Furthermore, in the COVID-19 pandemic, masks hide a significant portion of the face. In this work, we present BRITTANY, a biometric recognition tool through gait analysis using Laser Imaging Detection and Ranging (LIDAR) data and a Convolutional Neural Network (CNN). A Proof of Concept (PoC) has been carried out in an indoor environment with five users to evaluate BRITTANY. A new CNN architecture is presented, allowing the classification of aggregated occupancy maps that represent the people's gait. This new architecture has been compared with LeNet-5 and AlexNet through the same datasets. The final system reports an accuracy of 88%. | es_ES |
dc.language | eng | es_ES |
dc.publisher | Nature | es_ES |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Ingeniería de sistemas | es_ES |
dc.subject.other | Tracking | es_ES |
dc.subject.other | Gait analysis | es_ES |
dc.subject.other | Biometric recognition | es_ES |
dc.title | Biometric recognition through gait analysis | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.identifier.doi | 10.1038/S41598-022-18806-4 | |
dc.description.peerreviewed | SI | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.essn | 2045-2322 | |
dc.journal.title | Scientific Reports | es_ES |
dc.volume.number | 12 | es_ES |
dc.issue.number | 1 | es_ES |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |
dc.subject.unesco | 1203.25 Diseño de Sistemas Sensores | es_ES |
dc.description.project | Instituto Nacional de Ciberseguridad de Espana (INCIBE) | es_ES |
dc.description.project | The research described in this article has been funded by the Instituto Nacional de Ciberseguridad de España (INCIBE), under the grant ”ADENDA 4: Detección de nuevas amenazas y patrones desconocidos (Red Regional de Ciencia y Tecnología)”, addendum to the framework agreement INCIBE-Universidad de León, 2019-2021. Miguel Ángel González-Santamarta would like to thank Universidad de León for its funding support for his doctoral studies. | es_ES |