Grant: Alliance Grants

Person identification is increasingly important in today's digital age, with physical and cyber threats becoming commonplace. Rapid improvements in deep learning have led to the development and adoption of video-based facial recognition systems in public areas such as airports, hospitals, and shopping malls. Still, substantial challenges remain, including lighting conditions, perspective, and visual occlusion due to clothing, personal protective equipment, or crowded spaces. Recently, these video-based systems have also come under scrutiny as lawsuits have alleged invasion of privacy, and as inherent biases in the algorithms (due to inequitable training processes) have been identified. This has left law enforcement, security agencies, and industry seeking new user access control solutions. As an alternative to facial recognition, gait recognition, which involves identifying people by the way they walk, has emerged. Research has led to rapid improvements in video-based gait recognition, again leveraging deep learning to extract skeletal models of gait. The reliance on vision-based modalities, however, means that many of the same challenges and concerns apply. Compared to video-based gait recognition, sensor-based methods (such as those enabled by pressure-sensitive flooring) eliminate the need for video capture, mitigating concerns over image reuse and avoiding the adverse effects of illumination, visibility, appearance changes, and occlusion. In this work, in partnership with Stepscan Technologies and CyberNB, we will install a novel pressure-sensitive flooring system leading up to a set of swipe card activated turnstiles at a new high-security access-controlled building. With this installation, we will develop a novel pressure-based gait recognition system for user authentication, and conduct a longitudinal, real-world evaluation. Combined with controlled in-lab experiments to explore and address confounding factors to the performance of this system, this project will generate a first-of-its-kind dataset for pressure-based gait analysis, new knowledge about gait biometrics and its confounding factors, advancements in machine learning of human movement, and a translational learning experience for trainees and industry partners.