Abstract:
Gait recognition aims to identify people from a distance by analyzing their walking style. Nevertheless, the efficacy of recognition drops significantly under cross-view ...Show MoreMetadata
Abstract:
Gait recognition aims to identify people from a distance by analyzing their walking style. Nevertheless, the efficacy of recognition drops significantly under cross-view and, appearance-based variations such as carrying and clothing. In this study, the performance of the MobileNet-V1 deep network is evaluated in various scenarios to address the cross-view gait recognition problem. In the first scenario, the fine-tuned MobileNet-V1 is evaluated on Gait Energy Images (GEI) as input data, while in the second scenario, the fine-tuned MobileNet-V1 is assessed with Optical Flows and masked RGB frames input data. In the last scenario, the first two scenarios are combined over a single fused deep network based on finetuned MobileNet-V1, and a single recognition process is performed using two different fused features data; GEI features with Optical Flow features, and GEI features with masked RGB frame features. In the evaluation process, a comprehensive data set for the cross-view gait recognition problem, CASIA-B is used for the experiments. The obtained results demonstrate that in the last scenario, the contribution of masked RGB frame features to the recognition rate of GEI is more significant.
Published in: 2024 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)
Date of Conference: 04-06 September 2024
Date Added to IEEE Xplore: 24 September 2024
ISBN Information: