Deep Learning-based Intelligent Facial and Human Gait Recognition System

The aim of this project is to develop deep learning based real-time face detection from video footage for the purpose of identifying gait patterns and detecting facial recognition (FR). Nowadays, identity verification serves as an impenetrable technological barrier to deter crime. In this area, several approaches have been presented, although some issues remain unresolved. Using a variety of feature extraction and face identification techniques, the goal of this research is to create a CCTV video-based facial and gait recognition system. The system's most crucial components are face identification, localization, and identification. Convolutional neural networks (CNNs) in deep learning have focused on facial recognition, leading to the development of the E-CNN algorithm. The technology uses real-time surroundings or video datasets to obtain highlighted face data. Lastly, Compare the face photos and movement patterns (such as walking, running, and fall points) in the database with the extracted real-time face video data. A security alert or signal is issued to notify security personnel to take appropriate action if a discrepancy is found between the alert and the current data. The suggested system outperforms current technologies in terms of cost, accuracy, and efficiency.