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oai:repisalud.isciii.es:20.500.12105/182362024-11-28T15:25:24Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16927

00925njm 22002777a 4500 dc Baltanas, Samuel-Felipe author Ruiz-Sarmiento, Jose-Raul author Gonzalez-Jimenez, Javier author 2021-01-19 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. http://hdl.handle.net/10668/3965 http://hdl.handle.net/20.500.12105/18236 33477884 10.3390/s21020659 1424-8220 Sensors Face recognition Assistant mobile robots Cross-pose face recognition MAPIR Faces Human-robot interaction Reconocimiento facial Redes neurales de la computación Improving the Head Pose Variation Problem in Face Recognition for Mobile Robots