Proposal
SRS
SDD
Thesis
Team Members

Marwan Mohamed
Team Leader

John Hany
Team Member

Mariam Adham
Team Member

Alaa Waleed
Team Member
Supervisors

Dr. Walaa Hassan El-Ashmawi
Associate Professor

Eng. Shereen Essam
Teaching Assistant

Eng. Verina Saber
Teaching Assistant

Abstract
Gait recognition is the systematic study of locomotion, more specifically the study of human motion, using the eye and the brain of observers, expanded by instruments for measuring body movements, body mechanics, and the action of the muscles. Gait recognition is utilized to evaluate and treat people with conditions influencing their capacity to walk. It is additionally commonly used in sports biomechanics to assist competitors to run more productively and to recognize movement-related issues in individuals with injuries. Gait acknowledgment is based on the notion that each person includes an unmistakable and peculiar way of walking. Human movement consists of synchronized movements of hundreds of muscles and joints. Many authentication methods were created over the years. Human gait recognition is one of these methods, where a person’s movement could be used for identifying a person. As a result, we aim to detect a system competent in performing recognition of people inferred from a video/image of a person walking using machine learning techniques

System Objectives
1. Gait recognition is proposed to show how behavioral walking characteristics can be used to recognize if they have a problem or not.
2. Is a behavioral biometric modality that identifies people based on their unique walking pattern.
3.Recognize a person by consequently extricating movement characteristics of the walking person in the video or extracted the standard features from an image.
4.The system is capable of identifying a person’s identity derived from a video sequence of a person walking.

System Scope
In this work, we focus on developing a low-cost, gait analysis system and gait identification to be used in unconstrained locations. The developed system studies lower body motion in terms of gait parameters and joint kinematics. This requires classification data to normal and abnormal capture data. For this purpose, we propose to use a model obtained from an optical motion capture database.
.Develop a system capable of performing recognition of a person determined from a video of a person walking. The system should be able to store gait signatures of comparison to a later stage.
. Automatic extraction of important gait feature focuses should be accessible from a video in order to automate the classification process.
.Identify person identity from a video taken of a person. process.
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Proposal
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SRS
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SDD
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Thesis
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Accomplishments
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