Machine Perception and Intelligent Robotics Group (MAPIR), Department of System Engineering and Automation, Biomedical Research Institute of Malaga (IBIMA), University of Malaga, 29071 Málaga, Spain.
Sensors (Basel). 2021 Jan 19;21(2):659. doi: 10.3390/s21020659.
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.
人脸识别是机器人领域中具有巨大潜力的一项技术，因为它在人机交互（HRI）中起着重要作用。这种交互是成功部署需要定制协助的机器人的关键，例如教育和医疗保健领域，或协助人类完成日常任务。这些不受约束的环境为人脸识别带来了额外的困难，极端头部姿势变化是最具挑战性的问题之一。在本文中，我们解决了这个问题，并做出了四重贡献。首先，我们设计了一种工具，用于从一个人身上采集均匀分布的头部姿势图像，我们利用这些图像收集了一个新的人脸数据集，这些数据都在本文中展示。然后，该数据集被用作分析这个问题对许多最新方法的不利影响的测试平台，这些方法在超出一定姿势范围时效果会降低。最后，我们提出了一种优化方法，通过在识别系统的已知人脸集中考虑关键姿势样本来减轻这种负面影响。进行的实验表明，这种优化的姿势集显著提高了基于多任务级联卷积神经网络（MTCNNs）和弧面（ArcFace）的最新尖端系统的性能。
