Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework  

作　　者：Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan Nouf Abdullah Almujally Taerang Kim Jae-Hyuk Cha 

机构地区：[1]Department of Electrical Engineering,HITEC University,Taxila,47080,Pakistan [2]Department of Computer Science and Mathematics,Lebanese American University,Beirut,Lebanon [3]Department of Computer Science,HITEC University,Taxila,47080,Pakistan [4]Department of Information Systems,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia [5]Department of Computer Science,Hanyang University,Seoul,04763,Korea

出　　处：《Computers, Materials & Continua》2024年第1期357-374,共18页计算机、材料和连续体（英文）

基　　金：supported by the“Human Resources Program in Energy Technol-ogy”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and Granted Financial Resources from the Ministry of Trade,Industry,and Energy,Republic of Korea(No.20204010600090);The funding of this work was provided by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.

摘　　要：The demand for a non-contact biometric approach for candidate identification has grown over the past ten years.Based on the most important biometric application,human gait analysis is a significant research topic in computer vision.Researchers have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far away.Gait recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access control.These systems require a complex combination of technical,operational,and definitional considerations.The employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked applications.Thiswork proposes a novel deep learning-based framework for human gait classification in video sequences.This framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing clothes.The proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer learning.Next,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)algorithm.Then,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization algorithm.This algorithm chooses the best features,combined in a novel correlation-based fusion technique.Finally,the fused best features are categorized using medium,bi-layer,and tri-layered neural networks.On the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was achieved.The achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this work.

关 键 词：Gait recognition covariant factors BIOMETRIC deep learning FUSION feature selection 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]