GAIT analysis based on GENDER detection using pre-trained models and tune parameters

Abstract

Abstract In past several decades, gait biometrics has emerged as a viable alternative to traditional identification methods, offering advancements in surveillance, monitoring, and analysis techniques. However, determining gender based on gait remains a challenge, particularly in computer vision applications. This study proposes a robust and adaptable approach to address this issue by leveraging gait analysis. There is a growing need for datasets tailored to gait analysis and recognition to facilitate the extraction of relevant data. While most existing research relies on image-based gait datasets, this study utilizes the OULP-Age dataset from OU-ISIR, representing gait through gait energy images (GEIs). The methodology involves feature extraction from GEIs using pre-trained models, followed by classification with the XGBoost classifier. Gender prediction is enhanced through parameter fine-tuning of the XGBoost classifier. Comparative analysis of 11 pre-trained models for feature extraction reveals that DenseNet models, combined with optimized XGBoost parameters, demonstrate promising results for gender prediction. This study contributes to advancing gender prediction based on gait analysis and underscores the efficacy of integrating deep learning models with traditional classifiers for improved accuracy and reliability.

Keywords

• Pre-trained models
• Gait analysis
• Tune models
• Machine learning
• DenseNet
• Deep learning