RT Journal Article
T1 Improving the Head Pose Variation Problem in Face Recognition for Mobile Robots
A1 Baltanas, Samuel-Felipe
A1 Ruiz-Sarmiento, Jose-Raul
A1 Gonzalez-Jimenez, Javier
K1 Face recognition
K1 Assistant mobile robots
K1 Cross-pose face recognition
K1 MAPIR Faces
K1 Human-robot interaction
K1 Reconocimiento facial
K1 Redes neurales de la computación
AB 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.
PB MDPI
YR 2021
FD 2021-01-19
LK http://hdl.handle.net/10668/3965
UL http://hdl.handle.net/10668/3965
LA en
NO 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
DS RISalud
RD Apr 5, 2025