Gait Recognition via Cross Walking Condition Constraint

Abstract

Gait recognition is a biometric technique that captures human walking pattern using gait silhouettes as input and can be used for long-term recognition. Recently proposed video-based methods achieve high performance. However, gait covariates or walking conditions, i.e., bag carrying and clothing, make the recognition of intra-class gait samples hard. Advanced methods simply use triplet loss for metric learning, which does not take the gait covariates into account. For alleviating the adverse influence of gait covariates, we propose cross walking condition constraint to explicitly consider the gait covariates. Specifically, this approach designs center-based and pair-wise loss functions to decrease discrepancy of intra-class gait samples under different walking conditions and enlarge the distance of inter-class gait samples under the same walking condition. Besides, we also propose a video-based strong baseline model of high performance by applying simple yet effective tricks, which have been validated in other individual recognition fields. With the proposed baseline model and loss functions, our method achieves the state-of-the-art performance.

Gait Recognition Based on the Plantar Pressure Data of Ice and Snow Athletes

“…Among them, r xy (k) ∈ [−1, 1] is the correlation coefficient obtained after the correlation degree of the two signals to be analyzed is analyzed and then normalized [20]. It takes the rotation angle changes of the left and right leg joints as two sets of motion sampling data, and substitutes them into formula (19) for calculation and analysis.…”