Publication:
Improving the Head Pose Variation Problem in Face Recognition for Mobile Robots

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Date

2021-01-19

Authors

Baltanas, Samuel-Felipe
Ruiz-Sarmiento, Jose-Raul
Gonzalez-Jimenez, Javier

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MDPI
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Abstract

Face recognition is a technology with great potential in the field of robotics, due to its prominent role in human-robot interaction (HRI). This interaction is a keystone for the successful deployment of robots in areas requiring a customized assistance like education and healthcare, or assisting humans in everyday tasks. These unconstrained environments present additional difficulties for face recognition, extreme head pose variability being one of the most challenging. In this paper, we address this issue and make a fourfold contribution. First, it has been designed a tool for gathering an uniform distribution of head pose images from a person, which has been used to collect a new dataset of faces, both presented in this work. Then, the dataset has served as a testbed for analyzing the detrimental effects this problem has on a number of state-of-the-art methods, showing their decreased effectiveness outside a limited range of poses. Finally, we propose an optimization method to mitigate said negative effects by considering key pose samples in the recognition system's set of known faces. The conducted experiments demonstrate that this optimized set of poses significantly improves the performance of a state-of-the-art, cutting-edge system based on Multitask Cascaded Convolutional Neural Networks (MTCNNs) and ArcFace.

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Medical Subject Headings::Anatomy::Body Regions::Head::Face
Medical Subject Headings::Anatomy::Body Regions::Head
Medical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans
Medical Subject Headings::Information Science::Information Science::Computing Methodologies::Artificial Intelligence::Neural Networks (Computer)
Medical Subject Headings::Disciplines and Occupations::Natural Science Disciplines::Physics::Electronics::Robotics
Medical Subject Headings::Health Care::Health Services Administration::Patient Care Management::Delivery of Health Care

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Keywords

Face recognition, Assistant mobile robots, Cross-pose face recognition, MAPIR Faces, Human-robot interaction, Reconocimiento facial, Redes neurales de la computación

Citation

Baltanas SF, Ruiz-Sarmiento JR, Gonzalez-Jimenez J. Improving the Head Pose Variation Problem in Face Recognition for Mobile Robots. Sensors (Basel). 2021 Jan 19;21(2):659