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Human gait recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

Computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan Nouf Abdullah Almujally Taerang Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer *** 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 *** recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access *** systems require a complex combination of technical,operational,and definitional *** employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked *** proposes a novel deep learning-based framework for human gait classification in video *** framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing *** proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer ***,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)***,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization *** algorithm chooses the best features,combined in a novel correlation-based fusion ***,the fused best features are categorized using medium,bi-layer,and tri-layered neural *** 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 accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this wor

关键词： gait recognition covariant factors biometric deep learning fusion feature selection

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gait recognition via Cross Walking Condition Constraint

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Computers, Materials & Continua 2021年第9期68卷 3045-3060页

作者： Runsheng Wang Hefei Ling Ping Li Yuxuan Shi Lei Wu Jialie Shen HuaZhong University of Science and Technology Wuhan430074China Queen’s University BelfastBT71NNUK

gait recognition is a biometric technique that captures human walking pattern using gait silhouettes as input and can be used for long-term *** proposed video-based methods achieve high ***,gait covariates or walking conditions,i.e.,bag carrying and clothing,make the recognition of intra-class gait samples *** methods simply use triplet loss for metric learning,which does not take the gait covariates into *** alleviating the adverse influence of gait covariates,we propose cross walking condition constraint to explicitly consider the gait ***,this approach designs center-based and pair-wise loss functions to decrease discrepancy of intra-class gait samples under different walking conditions and enlarge the distance of inter-class gait samples under the same walking ***,we also propose a video-based strong baseline model of high performance by applying simple yet effective tricks,which have been validated in other individual recognition *** the proposed baseline model and loss functions,our method achieves the state-of-the-art performance.

关键词： gait recognition metric learning cross walking condition constraint gait covariates

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gait recognition System in Thermal Infrared Night Imaging by Using Deep Convolutional Neural Networks

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Journal of Donghua University(English Edition) 2021年第6期38卷 527-538页

作者： MANSSOR Samah A F SUN Shaoyuan ZHAO Guoshun QU Binjie College of Information Science & Technology Donghua University Shanghai 201620 China Faculty of Engineering and Technology University of Gezira Wad Madni 22211 Sudan

gait is an essential biomedical feature that distinguishes individuals through *** feature automatically stimulates the need for remote human recognition in security-sensitive visual monitoring ***,there is still a lack of sufficient accuracy of gait recognition at night,in addition to taking some critical factors that affect the performances of the recognition ***,a novel approach is proposed to automatically identify individuals from thermal infrared(TIR)images according to their gaits captured at *** approach uses a new night gait network(NgaitNet)based on similarity deep convolutional neural networks(CNNs)method to enhance gait recognition at ***,the TIR image is represented via personal movements and enhanced body skeleton ***,the state-space method with a Hough transform is used to extract gait features to obtain skeletal joints′*** features are trained to identify the most discriminating gait patterns that indicate a change in human *** verify the proposed method,the experimental results are performed by using learning and validation curves via being connected by the Visdom *** proposed thermal infrared imaging night gait recognition(TIRNgaitNet)approach has achieved the highest gait recognition accuracy rates(99.5%,97.0%),especially under normal walking conditions on the Chinese Academy of Sciences Institute of Automation infrared night gait dataset(CASIA C)and Donghua University thermal infrared night gait database(DHU night gait dataset).On the same dataset,the results of the TIRNgaitNet approach provide the record scores of(98.0%,87.0%)under the slow walking condition and(94.0%,86.0%)for the quick walking condition.

关键词： gait recognition thermal infrared(TIR)image silhouette feature extraction convolutional neural network(CNN)

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Human gait recognition Based on Kernel PCA Using Projections

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Journal of Computer Science & Technology 2007年第6期22卷 867-876页

作者： Murat Ekinci Murat Aykut Computer Vision Lab Department of Computer EngineeringKaradeniz Technical University61080 TrabzonTurkey

This paper presents a novel approach for human identification at a distance using gait recognition. recognition of a person from their gait is a biometric of increasing interest. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis （PCA）, to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. Fourier transform is performed to achieve translation invariant for the gait patterns accumulated from silhouette sequences which are extracted from different circumstances. Kernel PCA is then used to extract higher order relations among the gait patterns for future recognition. A fusion strategy is finally executed to produce a final decision. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles.

关键词： biometrics gait recognition gait representation kernel PCA pattern recognition

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A Multi-View gait recognition Method Using Deep Convolutional Neural Network and Channel Attention Mechanism

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Computer Modeling in Engineering & Sciences 2020年第10期125卷 345-363页

作者： Jiabin Wang Kai Peng College of Engineering Huaqiao UniversityQuanzhou362021China

In many existing multi-view gait recognition methods based on images or video sequences,gait sequences are usually used to superimpose and synthesize images and construct energy-like ***,information may be lost during the process of compositing image and capture EMG *** and the recognition accuracy may be introduced and affected respectively by some factors such as period *** better solve the problems,a multi-view gait recognition method using deep convolutional neural network and channel attention mechanism is ***,the sliding time window method is used to capture EMG ***,the back-propagation learning algorithm is used to train each layer of convolution,which improves the learning ability of the convolutional neural ***,the channel attention mechanism is integrated into the neural network,which will improve the ability of expressing gait *** a classifier is used to classify *** can be shown from experimental results on two public datasets,OULP and CASIA-B,the recognition rate of the proposed method can be achieved at 88.44%and 97.25%*** can be shown from the comparative experimental results,the proposed method has better recognition effect than several other newer convolutional neural network ***,the combination of convolutional neural network and channel attention mechanism is of great value for gait recognition.

关键词： EMG signal capture channel attention mechanism convolutional neural network multi-view gait recognition gait characteristics back-propagation

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Human gait recognition Using Deep Learning and Improved Ant Colony Optimization

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Computers, Materials & Continua 2022年第2期70卷 2113-2130页

作者： Awais Khan Muhammad Attique Khan Muhammad Younus Javed Majed Alhaisoni Usman Tariq Seifedine Kadry Jung-In Choi Yunyoung Nam Department of Computer Science HITEC University TaxilaTaxila47080Pakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Human gait recognition(HGR)has received a lot of attention in the last decade as an alternative biometric *** main challenges in gait recognition are the change in in-person view angle and covariant *** major covariant factors are walking while carrying a bag and walking while wearing a *** learning is a new machine learning technique that is gaining *** techniques for HGR based on deep learning are presented in the *** requirement of an efficient framework is always required for correct and quick gait *** proposed a fully automated deep learning and improved ant colony optimization(IACO)framework for HGR using video sequences in this *** proposed framework consists of four primary *** the first step,the database is normalized in a video *** the second step,two pre-trained models named ResNet101 and InceptionV3 are selected andmodified according to the dataset’s *** that,we trained both modified models using transfer learning and extracted the *** IACO algorithm is used to improve the extracted *** is used to select the best features,which are then passed to the Cubic SVM for final *** cubic SVM employs a multiclass *** experiment was carried out on three angles(0,18,and 180)of the CASIA B dataset,and the accuracy was 95.2,93.9,and 98.2 percent,respectively.A comparison with existing techniques is also performed,and the proposed method outperforms in terms of accuracy and computational time.

关键词： gait recognition deep learning transfer learning features optimization classification

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Probabilistic Model-Based Silhouette Refinement for gait recognition

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Journal of Shanghai Jiaotong university(Science) 2010年第1期15卷 24-30页

作者：张元元吴晓娟阮秋琦 Institute of Image Processing and Pattern recognition Shandong University Institute of Information Science Beijing Jiaotong University

An algorithm to refine and clean gait silhouette noises generated by imperfect motion detection techniques is developed,and a relatively complete and high quality silhouette is *** silhouettes are sequentially refined in two levels according to two different probabilistic *** first level is within-sequence *** silhouette in a particular sequence is refined by an individual model trained by the gait images from current *** second level is between-sequence *** the silhouettes that need further refinement are modified by a population model trained by the gait images chosen from a certain amount of *** intention is to preserve the within-class similarity and to decrease the interaction between one class and *** experimental results indicate that the proposed algorithm is simple and quite effective,and it helps the existing recognition methods achieve a higher recognition performance.

关键词： silhouette refinement probabilistic model gait recognition performance evaluation

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Interframe Variation Vector:A Novel Feature for gait recognition

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Journal of Donghua University(English Edition) 2010年第2期27卷 233-236页

作者：苏松志王丽李绍滋 Department of Cognitive Science Fujian Key Laboratory of the Brain-like Intelligent SystemsXiamen University Xiamen Software College

gait representation is an important issue in gait recognition. A simple yet efficient approach, called Interframe Variation Vector （IW）, is proposed. IW considers the spatiotemporal motion characteristic of gait, and uses the shape variation information between successive frames to represent gait signature. Different from other features, IVV rather than condenses a gait sequence into single image resulting in spatial sequence lost; it records the whole moving process in an IVV sequence. IVV can encode whole essential features of gait and preserve all the movements of limbs. Experimental results show that the proposed gait representation has a promising recognition performance.

关键词： gait recognition human identification Interframe Variation Vector

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gait recognition based on Wasserstein generating adversarial image inpainting network

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Journal of Central South University 2019年第10期26卷 2759-2770页

作者： XIA Li-min WANG Hao GUO Wei-ting School of Automation Central South University

Aiming at the problem of small area human occlusion in gait recognition,a method based on generating adversarial image inpainting network was proposed which can generate a context consistent image for gait occlusion *** order to reduce the effect of noise on feature extraction,the stacked automatic encoder with robustness was *** order to improve the ability of gait classification,the sparse coding was used to express and classify the gait *** results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA-B and TUM-GAID for gait recognition.

关键词： gait recognition image inpainting generating adversarial network stacking automatic encoder

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Novel Walking Stability-Based gait recognition Method for Functional Electrical Stimulation System Control

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Transactions of Tianjin University 2007年第2期13卷 93-97页

作者：明东万柏坤胡勇汪曣王威杰吴英华陆瓞骥 School of Precision Instrument and Opto-Electronics Engineering Tianjin University Department of Orthopaedics and Traumatology Hong Kong University Department of Orthopaedic and Trauma Surgery University of Dundee Department of Traumatology Orthopaedics Tianjin Hospital

gait recognition is the key question of functional electrical stimulation (FES) system control for paraplegic walking. A new risk-tendency-graph (RTG) method was proposed to recognize the stability information in FES-assisted walking gait. The main instrument was a specialized walker dynamometer system based on a multi-channel strain-gauge bridge network fixed on the walker frame. During walking process, this system collected the reaction forces between patient's upper extremities and walker and converted them into RTG morphologic curves of dynamic gait stability in temporal and spatial domains. To demonstrate the potential usefulness of RTG, preliminary clinical trials were done with paraplegic patients. The gait stability levels of two walking cases with 4- and 12-week FES training from one subject were quantified (0.43 and 0.19) from the results of temporal and spatial RTG. Relevant instable phases in gait cycle and dangerous inclinations of patient's body during walking process were also brought forward. In conclusion, the new RTG method is practical for distinguishing more useful gait stability information for FES system control.

关键词： gait recognition functional electrical stimulation parapegic walking risk-tendency-graph

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