Enhancing the robustness of forensic gait analysis against near-distance viewing direction differences

Abstract

Gait analysis is a promising biometric technology to visually and quantitatively analyze an individual’s walking style. In Japan, silhouette-based quantitative gait analyses have been implemented as a forensic tool; however, several challenges remain owing the narrow range of application. One of the yet-unsolved issues pertains to the existence of a ‘slight’ but critical viewing direction difference, which leads to the incorrect judgment in the analyses of a person even when using deep learning-based feature extraction. To alleviate the critical viewing direction difference problem, we developed a novel gait analysis technique involving three components: 3D calibration, gait energy image space registration, and regression of the distance vector. Results of the GUI development and mock appraisal tests indicated that the proposed method can help achieve practical improvements in the forensic science domain.