Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/73852
Título: AveroBot: an audio-visual dataset for people re-identification and verification in human-robot interaction
Autores/as: Marras, Mirko
Marín-Reyes, Pedro A. 
Lorenzo Navarro, José Javier 
Castrillón Santana, Modesto Fernando 
Fenu, Gianni
Clasificación UNESCO: 120304 Inteligencia artificial
Palabras clave: Deep learning
Face-voice dataset
Human-robot interaction
People re-identification
People verification
Fecha de publicación: 2019
Proyectos: Identificación Automática de Oradores en Sesiones Parlamentarias Usando Características Audiovisuales. 
Publicación seriada: ICPRAM (Setúbal) 
Conferencia: 8th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2019 
Resumen: Intelligent technologies have pervaded our daily life, making it easier for people to complete their activities. One emerging application is involving the use of robots for assisting people in various tasks (e.g., visiting a museum). In this context, it is crucial to enable robots to correctly identify people. Existing robots often use facial information to establish the identity of a person of interest. But, the face alone may not offer enough relevant information due to variations in pose, illumination, resolution and recording distance. Other biometric modalities like the voice can improve the recognition performance in these conditions. However, the existing datasets in robotic scenarios usually do not include the audio cue and tend to suffer from one or more limitations: most of them are acquired under controlled conditions, limited in number of identities or samples per user, collected by the same recording device, and/or not freely available. In this paper, we propose AveRobot, an audio-visual dataset of 111 participants vocalizing short sentences under robot assistance scenarios. The collection took place into a three-floor building through eight different cameras with built-in microphones. The performance for face and voice re-identification and verification was evaluated on this dataset with deep learning baselines, and compared against audio-visual datasets from diverse scenarios. The results showed that AveRobot is a challenging dataset for people re-identification and verification.
URI: http://hdl.handle.net/10553/73852
ISBN: 978-989-758-351-3
ISSN: 2184-4313
DOI: 10.5220/0007690902550265
Fuente: Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, p. 255-265 (2019)
Colección:Actas de congresos
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