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Authors: Mirko Marras 1 ; Pedro Marín-Reyes 2 ; Javier Lorenzo-Navarro 2 ; Modesto Castrillón-Santana 2 and Gianni Fenu 1

Affiliations: 1 Department of Mathematics and Computer Science, University of Cagliari, V. Ospedale 72, 09124 Cagliari and Italy ; 2 Instituto Universitario Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (SIANI), Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria and Spain

ISBN: 978-989-758-351-3

Keyword(s): Face-voice Dataset, Deep Learning, People Verification, People Re-Identification, Human-Robot Interaction.

Related Ontology Subjects/Areas/Topics: Applications ; Biomedical Engineering ; Biomedical Signal Processing ; Biometrics ; Biometrics and Pattern Recognition ; Multimedia ; Multimedia Signal Processing ; Pattern Recognition ; Robotics ; Software Engineering ; Telecommunications

Abstract: 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. (More)

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Paper citation in several formats:
Marras, M.; Marín-Reyes, P.; Lorenzo-Navarro, J.; Castrillón-Santana, M. and Fenu, G. (2019). AveRobot: An Audio-visual Dataset for People Re-identification and Verification in Human-Robot Interaction.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 255-265. DOI: 10.5220/0007690902550265

@conference{icpram19,
author={Mirko Marras. and Marín{-}Reyes, P. and Javier Lorenzo{-}Navarro. and Modesto Castrillón{-}Santana. and Gianni Fenu.},
title={AveRobot: An Audio-visual Dataset for People Re-identification and Verification in Human-Robot Interaction},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={255-265},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007690902550265},
isbn={978-989-758-351-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - AveRobot: An Audio-visual Dataset for People Re-identification and Verification in Human-Robot Interaction
SN - 978-989-758-351-3
AU - Marras, M.
AU - Marín-Reyes, P.
AU - Lorenzo-Navarro, J.
AU - Castrillón-Santana, M.
AU - Fenu, G.
PY - 2019
SP - 255
EP - 265
DO - 10.5220/0007690902550265

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