GaitHF: Height Fused Gait Recognition

Abstract Gait recognition, an emerging field at the intersection of computer vision and biometrics, has garnered significant attention for its potential applications in surveillance, security, and healthcare. In this paper, we present a novel method that combines appearance-based gait recognition with human height data. The proposed approach aims to enhance the accuracy and robustness of gait recognition systems by incorporating complementary features derived from both gait patterns and human height. We believe that incorporating height information can offer additional discriminative power to gait recognition models, enabling them to better distinguish individuals in various scenarios. Many gait recognition convolutional neural networks using deep learning methods have made good data progress in recent years, so we also adopt this approach, e.g., deep learning methods such as (CNN) and Recurrent Neural Networks (RNN), which can automatically learn hierarchical representations of gait and height features to capture intricate patterns and relationships. Our experiments involve a comprehensive analysis using benchmark gait datasets, demonstrating the effectiveness of the proposed approach in comparison to traditional gait recognition methods. The results highlight the potential of leveraging human height information to enhance the overall performance of gait recognition systems. Our experimental data show that the results achieved by many appearance-based gait recognition models on the CASIA-B and OU-MVLP datasets progress in most conditions after using our proposed new approach, which are eye-catching in that the average accuracy improves by 1.875% and 6% on BG and CL of CASIA-B, respectively, and the average accuracy improves on the large dataset OU-MVLP is also improved by 1.35%.