PerFication: A Person Identifying Technique by Evaluating Gait with 2D LiDAR Data

Abstract

PerFication is a person identification technique that uses a 2D LiDAR sensor in a customized dataset KoLaSu (Kobayashi Laboratory of Saitama University). Video-based recognition systems are highly effective and are now at the forefront of research. However, it experiences bottlenecks. New inventions can cause embarrassing situations, settings, and momentum. To address the limitations of technology, one must introduce a new technology to enhance it. Using biometric characteristics are highly reliable and valuable methods for identifying individuals. Most approaches depend on close interactions with the subject. A gait is the walking pattern of an individual. Most research on identifying individuals based on their walking patterns is conducted using RGB or RGB-D cameras. Only a limited number of studies utilized LiDAR data. Working with 2D LiDAR imagery for individual tracking and identification is excellent in situations where video monitoring is ineffective, owing to environmental challenges such as disasters, smoke, occlusion, and economic constraints. This study presented an extensive analysis of 2D LiDAR data using a meticulously created dataset and a modified residual neural network. In this paper, an alternative method of person identification is proposed that circumvents the limitations of video cameras in terms of capturing difficulties. An individual is precisely identified by the system through the utilization of ankle-level 2D LiDAR data. Our LiDAR-based detection system offers a unique method for person identification in modern surveillance systems, with a painstaking dataset, remarkable results, and a break from traditional camera setups. We focused on demonstrating the cost-effectiveness and durability of LiDAR sensors by utilizing 2D sensors in our research.