Identifying Humans Through Gait Features

Achieving robust human identification in visual surveillance is an ongoing and open research challenge in biometrics. In recent years, gait has added attention for its unique benefits when matched to other biometrics. Different gait-challenging conditions hinder the performance of gait recognition systems in real-world scenarios. The only solution to solve these challenges is to develop suitable features using available information sources. Enhancing the gait recognition system’s performance is the goal of this research, with a focus on frontal, speed-invariant, and clothing-invariant recognition. The proposed approaches demonstrate their capabilities through experimental results, outperforming existing methods of gait recognition. The solutions proposed in this paper increase gait recognition performance, making it applicable in real-world scenarios.