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Gender recognition based on gait features has gained significant interest due to its wide range of applications in various fields. This paper proposes GenReGait, a robust method for gender recognition utilizing gait features. Gait, the unique walking pattern of individuals, contains distinct gender-specific characteristics, such as stride length, step frequency, and body posture, making it a promising modality for gender estimation. The proposed GenReGait method begins by extracting landmark positions on the human body using a human keypoint estimation technique. These landmarks serve as informative cues for estimating gender based on their spatial and temporal characteristics. However, environmental factors can impact gait patterns and introduce fluctuations in landmark points, affecting the accuracy of gender estimation. To overcome this challenge, GenReGait introduces a robust preprocessing technique known as Weighted Exponential Moving Average to smoothen the gait signals and reduce noise caused by environmental factors. The smoothed signals are then fed into a deep learning network trained to perform gender estimation based on the gait features extracted from the landmark positions. By leveraging deep learning algorithms, the proposed GenReGait method effectively captures complex patterns and relationships within the gait features, enhancing the accuracy and reliability of gender recognition. Experimental evaluations conducted on the Gait in the Wild dataset and a self-collected dataset validate the robustness and effectiveness of the proposed GenReGait approach.
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