Enhancing Gait Recognition through Edge Detection and Hybrid Feature Extraction Techniques

Abstract

Gait recognition is the process of identifying individuals based on their unique walking patterns. This method has gained importance in biometric systems for its non-intrusive nature and applications in security, surveillance, and healthcare, where it helps monitor and identify individuals from a distance. However, accurately distin- guishing between gait patterns under varying conditions poses significant challenges. These include variations in walking speed, changes in clothing and footwear, and different environmental conditions, which all affect the accuracy of gait recognition systems. Moreover, the lack of diverse and high-quality datasets complicates the development of robust models that generalize well across different populations. To overcome these challenges, we have developed a novel approach that incorpo- rates various preprocessing techniques, such as edge detection, contrast enhancement, and noise reduction, with feature selection methods to improve data quality and model performance. Our hybrid feature extraction model combines Kolmogorov-Arnold Net- works (KANs), ResNet, EfficientNet, and Principal Component Analysis (PCA) for spatiotemporal features, with traditional methods like Histogram of Oriented Gradi- ents (HOG) and Local Binary Patterns (LBP). This approach bridges the research gap by demonstrating improved performance on the Chinese Academy of Sciences In- stitute of Automation (CASIA) datasets A, B, and C, enhancing robustness. These advancements are particularly promising for real-time applications in healthcare, such as early detection of neurological conditions like Alzheimer’s disease (AD). Evaluations using metrics such as Accuracy, F1 Score, and Area Under the Curve (AUC) have been conducted. The results show that the KAN model achieved the highest overall accuracy, surpassing 95%, while the ResNet model excelled across all metrics, effectively handling complex data variations. Integrating traditional and deep learning features boosts model accuracy and robustness. Comparative analysis with existing techniques confirms that our proposed models outperform previous methods in key metrics, validating the hypothesis that integrating diverse features and robust training strategies can substantially enhance gait recognition systems.