Gender Recognition in Walk Gait through 3D Motion by Quadratic Bezier Curve and Statistical Techniques

TL;DR

This paper presents a novel method for gender recognition based on 3D gait analysis using quadratic Bezier curves and statistical techniques, aiming to improve accuracy in motion capture applications.

Contribution

It introduces a new approach combining quadratic Bezier curve modeling with statistical analysis for gender classification from gait data.

Findings

Achieved high accuracy in gender recognition from 3D gait data.

Demonstrated effectiveness of quadratic Bezier curves in modeling gait features.

Validated the method on motion capture datasets.

Abstract

Motion capture is the process of recording the movement of objects or people. It is used in military, entertainment, sports, and medical applications, and for validation of computer vision[2] and robotics. In filmmaking and video game development, it refers to recording actions of human actors, and using that information to animate digital character models in 2D or 3D computer animation. When it includes face and fingers or captures subtle