Advanced Machine Learning Techniques for Gait Recognition: An In-Depth Exploration of Biometric Analysis

This investigation pioneers a thorough exploration into the domain of gait recognition within biometric analysis, specifically addressing the challenges unique to gait as a biometric identifier. The study systematically analyzes an array of machine learning (ML) algorithms, encompassing methodologies such as Individual Node Evaluation, Statistical Inference, Regression Modeling, Supportive Vectorization Technique, Nearest Neighbor Classification, Decisional Tree Structure, Randomized Forest Ensemble, and Multilayered Node Connection. Rigorous testing is employed to evaluate the efficacy of each model in accurately identifying individuals based on their distinct gait patterns. The methodology prioritizes detailed preprocessing to ensure data integrity and relevance. Furthermore, the exploration involves employing Sequential Backward Selection (SBS) for feature selection and implementing dimensionality reduction techniques like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). This enhances the effectiveness of the model by concentrating on the most relevant features.The research further explores deep learning models, assessing various architectures to understand their impact on gait recognition accuracy. A meticulous comparative analysis highlights the strengths and limitations of each approach, offering unique insights to the field. By evaluating a range of ML and DL techniques, this study establishes a benchmark for future advancements in biometric security. It underscores the potential of gait recognition as a reliable, non-invasive identification method, laying the groundwork for the development of advanced and accurate biometric systems essential in the dynamic landscape of security and personal identification.