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research-article

A Face Recognition System for Assistive Robots

Authors:

Samuel-Felipe Baltanas,

Jose-Raul Ruiz-Sarmiento,

Javier Gonzalez-JimenezAuthors Info & Claims

APPIS 2020: Proceedings of the 3rd International Conference on Applications of Intelligent Systems

Article No.: 29, Pages 1 - 6

https://doi.org/10.1145/3378184.3378225

Published: 17 February 2020 Publication History

Abstract

Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience. To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance. In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots. Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet. We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS). The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform.

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Cited By

Cañete AQuemada-Torres ERuiz-Sarmiento JMoreno FGonzalez-Jimenez J(2024)Sistema multimodal para la orientación de robots móviles hacia su interlocutorJornadas de Automática10.17979/ja-cea.2024.45.10939Online publication date: 19-Jul-2024
https://doi.org/10.17979/ja-cea.2024.45.10939
Algabri RAbdu ALee S(2024)Deep learning and machine learning techniques for head pose estimation: a surveyArtificial Intelligence Review10.1007/s10462-024-10936-757:10Online publication date: 12-Sep-2024
https://doi.org/10.1007/s10462-024-10936-7
吴铎(2023)An Efficient Biometric Identification Privacy Protection Protocol on the CloudComputer Science and Application10.12677/CSA.2023.13916313:09(1641-1654)Online publication date: 2023
https://doi.org/10.12677/CSA.2023.139163
Show More Cited By

Index Terms

A Face Recognition System for Assistive Robots
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Biometrics
        Scene understanding
        Vision for robotics

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Published In

cover image ACM Other conferences

APPIS 2020: Proceedings of the 3rd International Conference on Applications of Intelligent Systems

January 2020

214 pages

ISBN:9781450376303

DOI:10.1145/3378184

Editors:
Nicolai Petkov
University of Groningen, The Netherlands
,
Nicola Strisciuglio
University of Twente, The Netherlands
,
Carlos M. Travieso-González
University of Las Palmas de Gran Canaria, Spain

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 February 2020

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Universidad de Málaga
European H2020 program
Spanish Government

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APPIS 2020

APPIS 2020: 3rd International Conference on Applications of Intelligent Systems

January 7 - 9, 2020

Las Palmas de Gran Canaria, Spain

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Cited By

Cañete AQuemada-Torres ERuiz-Sarmiento JMoreno FGonzalez-Jimenez J(2024)Sistema multimodal para la orientación de robots móviles hacia su interlocutorJornadas de Automática10.17979/ja-cea.2024.45.10939Online publication date: 19-Jul-2024
https://doi.org/10.17979/ja-cea.2024.45.10939
Algabri RAbdu ALee S(2024)Deep learning and machine learning techniques for head pose estimation: a surveyArtificial Intelligence Review10.1007/s10462-024-10936-757:10Online publication date: 12-Sep-2024
https://doi.org/10.1007/s10462-024-10936-7
吴铎(2023)An Efficient Biometric Identification Privacy Protection Protocol on the CloudComputer Science and Application10.12677/CSA.2023.13916313:09(1641-1654)Online publication date: 2023
https://doi.org/10.12677/CSA.2023.139163
Hammadi YGrondin FFerland FLebel K(2022)Evaluation of Various State of the Art Head Pose Estimation Algorithms for Clinical ScenariosSensors10.3390/s2218685022:18(6850)Online publication date: 10-Sep-2022
https://doi.org/10.3390/s22186850
Yang LTian CZhang GLi LWang H(2022)Efficient Biometric Identification on the Cloud With Privacy Preservation GuaranteeIEEE Access10.1109/ACCESS.2022.321870310(115520-115531)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3218703
Santos JMartins IRodrigues J(2021)Framework for Controlling KNX Devices Based on GesturesUniversal Access in Human-Computer Interaction. Access to Media, Learning and Assistive Environments10.1007/978-3-030-78095-1_37(507-518)Online publication date: 3-Jul-2021
https://doi.org/10.1007/978-3-030-78095-1_37

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