Department of Mechanical, Computer Science and Aerospace Engineering, University of León, 24071, León, Spain.
Sci Rep. 2022 Aug 25;12(1):14530. doi: 10.1038/s41598-022-18806-4.
The use of people recognition techniques has become critical in some areas. For instance, social or assistive robots carry out collaborative tasks in the robotics field. A robot must know who to work with to deal with such tasks. Using biometric patterns may replace identification cards or codes on access control to critical infrastructures. The usage of Red Green Blue Depth (RGBD) cameras is ubiquitous to solve people recognition. However, this sensor has some constraints, such as they demand high computational capabilities, require the users to face the sensor, or do not regard users' privacy. Furthermore, in the COVID-19 pandemic, masks hide a significant portion of the face. In this work, we present BRITTANY, a biometric recognition tool through gait analysis using Laser Imaging Detection and Ranging (LIDAR) data and a Convolutional Neural Network (CNN). A Proof of Concept (PoC) has been carried out in an indoor environment with five users to evaluate BRITTANY. A new CNN architecture is presented, allowing the classification of aggregated occupancy maps that represent the people's gait. This new architecture has been compared with LeNet-5 and AlexNet through the same datasets. The final system reports an accuracy of 88%.
人脸识别技术在某些领域变得至关重要。例如，社交或辅助机器人在机器人领域执行协作任务。机器人必须知道与谁合作来处理此类任务。使用生物识别模式可以替代关键基础设施的门禁识别卡或密码。使用红绿蓝深度（RGBD）相机来解决人脸识别问题已经非常普遍。然而，这种传感器存在一些限制，例如需要高计算能力、要求用户面对传感器或不考虑用户隐私。此外，在 COVID-19 大流行期间，口罩遮住了面部的很大一部分。在这项工作中，我们提出了 BRITTANY，这是一种通过使用激光成像检测和测距（LIDAR）数据和卷积神经网络（CNN）进行步态分析的生物识别工具。已经在一个有五个用户的室内环境中进行了概念验证（PoC）来评估 BRITTANY。提出了一种新的 CNN 架构，允许对表示人员步态的聚合占用图进行分类。通过相同的数据集，将新架构与 LeNet-5 和 AlexNet 进行了比较。最终系统的准确率为 88%。
