Gait Recognition under Speed Transition

Forensic Gait Analysis and Recognition: Standards of Evidence Admissibility

“…For example, a promising avenue is being taken by a team from Osaka University in Japan who have compiled the largest gait database of video and gait energy images (whole body shapes extracted from video) of 63,846 subjects (31,093 males and 32,753 females) with ages ranging from 2 to 90 years, the largest number of subjects being in the 6–15 years and 21–25 years age ranges and the lowest in the 51–90 years age range (approximately 2000 individuals in total) . Other large datasets also developed by this team include treadmill datasets which comprise speed and clothing variations and gait fluctuations , speed transition variation dataset , covariate (carrying objects) dataset , multiview camera angle dataset , and inertial sensors datasets . Although the datasets are varied and constitute a valuable step in gait recognition research, there are several limitations to consider.…”

“…In biometrics, gait is classified as a behavioral biometric , and the implication of this classification is that gait is not considered as individualistic as physiological biometrics (e.g., fingerprints), specifically because gait can be altered, albeit to a certain extent, by behavior. Inebriety, emotions, state of mind, and mood can all have an effect on a person's manner of walking, and implicitly, complicate the process of recognition, as do a variety of covariates such as clothing, shoes, carrying of items, type of walking surface, speed variation, among others . Thus, considering the large number of potential challenges which may arise, one may ask whether it is indeed worth considering gait as a biometric or even as a trait for forensic gait analysis.…”

Speed invariant gait recognition—The enhanced mutual subspace method

“…The walking speed change causes variations in pitch and stride, which result in non-invariant gait features. Various approaches have been proposed to tackle this issue [ 7 , 8 ], however the performance of these methods is not sufficient. For example, with a challenging data set such as OU-ISIR Gait Speed Transition Dataset [ 8 ], Mansur et al reported a correct classification ratio of 84 [%].…”

Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones

“…Most VIO methods are not robust to full occlusions. If foot-mounted sensors or cameras are not available, most inertial sensor based approaches for pedestrian deadreckoning (PDR) rely on step counting [11,12,13]. These methods typically assume horizontal 2D motion and are not applicable for wheeled motion or tracking pedestrians in 3D environments or in escalators and elevators.…”