Human Gait Recognition using Machine Learning Teachnolgies for Inclusive Innovation

Abstract

Gait recognition, or the ability to identify people based on how they walk, is used in a variety of contexts, including human computer interaction, security checks, and health monitoring. Due to Out of touch and uncooperative individuals, gait-based human recognition is an emerging behavioural biometric feature for intelligent surveillance monitoring. In video surveillance, gait recognition can be used to detect objects at a distance and assist in low-resolution object identification. Recent years have seen an explosion in the study of gait analysis for a wide range of uses, such as animation, video surveillance, health monitoring, and authentication. A sophisticated new technology called gait recognition can identify persons Shoot from a distance and perform well on movies with no resolution. This document provides various walkthroughs written in the form of examples and free samples. Four main processes make up the survey of gait detection algorithms covered in this study: feature extraction, classification, preprocessing, and data collection. The descriptions of the pathology database, Vision base, and wearable sensor are compiled. In recent years, deep architectures have made great progress in improving human recognition performance. This article provides an up-to-date overview of deep architectures for gait recognition, highlighting the use of convolutional neural networks along with other architectures. Furthermore, the overall problems of gait recognition are examined together with potential directions for future research.