Human Gait Recognition Technology

Human gait recognition is a biometric technology that identifies individuals based on their unique walking patterns. This emerging field utilizes computer vision techniques to capture and analyze various gait parameters including stride length, cadence, walking speed, foot angle at ground contact, and joint movements. The implementation typically involves preprocessing video frames using OpenCV or similar libraries to extract silhouette sequences, followed by feature extraction methods such as Gait Energy Image (GEI) or Chrono-Gait Image (CGI) representations. Machine learning algorithms like Support Vector Machines (SVM), Convolutional Neural Networks (CNN), or Recurrent Neural Networks (RNN) are then employed for pattern classification. Key functions include background subtraction for silhouette extraction, temporal normalization for consistent sequence length, and feature dimensionality reduction using PCA or LDA. While still in developmental stages, this technology shows significant promise for law enforcement applications, secure access control systems, and clinical gait analysis where non-intrusive identification is crucial for security and medical purposes.