Next-Gen Gait Recognition: Advanced Machine Learning for Precision Biometric Analysis
Authors:-Mr.Y.Ravi Bhushan, K.Charan Praveen Kumar, M.Sushma, T.Lasya Srivallika, Ch.Geetha Sri, K.D.V.Chaitanya
Abstract-Stroke, which ranks as the second leading cause of death worldwide, requires prompt and precise prediction for effective intervention. This study conducts a comprehensive exploration of gait recognition in biometric analysis, addressing the unique challenges of using gait as an identifier. It systematically evaluates various machine learning algorithms, including Individual Node Evaluation, Statistical Inference, Regression Modelling, Support Vector Machines, k-Nearest Neighbours, Decision Trees, Random Forests, and Neural Networks. Each model undergoes rigorous testing to assess its effectiveness in accurately identifying individuals based on their gait patterns. The methodology emphasizes thorough preprocessing to maintain data integrity and relevance, incorporating Sequential Backward Selection (SBS) for feature selection and dimensionality reduction techniques like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to enhance model efficiency. Additionally, the study explores deep learning architectures, analysing their impact on recognition accuracy. A detailed comparative analysis highlights the strengths and weaknesses of each approach, offering valuable insights into the field. By evaluating a range of ML and DL techniques, this research sets a benchmark for future advancements in biometric security, reinforcing gait recognition as a reliable, non-invasive identification method and paving the way for advanced biometric systems in security and personal identification.
