Human Gait Recognition: A Deep Learning and Best Feature Selection Framework

Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking surfaces. Furthermore, identification from differing views is a significant difficulty in HGR. Many techniques have been introduced in the literature for HGR using conventional and deep learning techniques. However, the traditional methods are not suitable for large datasets. Therefore, a new framework is proposed for human gait recognition using deep learning and best feature selection. The proposed framework includes data augmentation, feature extraction, feature selection, feature fusion, and classification. In the augmentation step, three flip operations were used. In the feature extraction step, two pre-trained models were employed, Inception-ResNet-V2 and NASNet Mobile. Both models were fine-tuned and trained using transfer learning on the CASIA B gait dataset. The features of the selected deep models were optimized using a modified three-step whale optimization algorithm and the best features were chosen. The selected best features were fused using the modified mean absolute deviation extended serial fusion (MDeSF) approach. Then, the final classification was performed using several classification algorithms. The experimental process was conducted on the entire CASIA B dataset and achieved an average accuracy of 89.0. Comparison with existing techniques showed an improvement in accuracy, recall rate, and computational time.

Download Full-text

Unsupervised feature selection method for improved human gait recognition

2015 23rd European Signal Processing Conference (EUSIPCO) ◽

10.1109/eusipco.2015.7362559 ◽

2015 ◽

Cited By ~ 6

Author(s):

Imad Rida ◽

Somaya Al Maadeed ◽

Ahmed Bouridane

Keyword(s):

Feature Selection ◽

Gait Recognition ◽

Feature Selection Method ◽

Selection Method ◽

Human Gait ◽

Unsupervised Feature Selection

Download Full-text

Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization

Computers Materials & Continua ◽

10.32604/cmc.2022.018270 ◽

2022 ◽

Vol 70 (2) ◽

pp. 2113-2130

Author(s):

Awais Khan ◽

Muhammad Attique Khan ◽

Muhammad Younus Javed ◽

Majed Alhaisoni ◽

Usman Tariq ◽

...

Keyword(s):

Deep Learning ◽

Ant Colony Optimization ◽

Gait Recognition ◽

Ant Colony ◽

Human Gait

Download Full-text

Human gait recognition based on Caffe deep learning framework

Proceedings of International Conference on Artificial Life and Robotics ◽

10.5954/icarob.2018.gs4-2 ◽

2018 ◽

Vol 23 ◽

pp. 109-111

Author(s):

Jiwu Wang ◽

Feng Chen

Keyword(s):

Deep Learning ◽

Gait Recognition ◽

Human Gait ◽

Learning Framework

Download Full-text

An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17165633 ◽

2020 ◽

Vol 17 (16) ◽

pp. 5633 ◽

Cited By ~ 1

Author(s):

Tao Zhen ◽

Lei Yan ◽

Jian-lei Kong

Keyword(s):

Deep Learning ◽

Information Acquisition ◽

Gait Recognition ◽

Medical Rehabilitation ◽

Identification Accuracy ◽

Sensor Data ◽

Human Gait ◽

Phase Detection ◽

Feature Extractor ◽

Gait Phase

Human-gait-phase-recognition is an important technology in the field of exoskeleton robot control and medical rehabilitation. Inertial sensors with accelerometers and gyroscopes are easy to wear, inexpensive and have great potential for analyzing gait dynamics. However, current deep-learning methods extract spatial and temporal features in isolation—while ignoring the inherent correlation in high-dimensional spaces—which limits the accuracy of a single model. This paper proposes an effective hybrid deep-learning framework based on the fusion of multiple spatiotemporal networks (FMS-Net), which is used to detect asynchronous phases from IMU signals. More specifically, it first uses a gait-information acquisition system to collect IMU sensor data fixed on the lower leg. Through data preprocessing, the framework constructs a spatial feature extractor with CNN module and a temporal feature extractor, combined with LSTM module. Finally, a skip-connection structure and the two-layer fully connected layer fusion module are used to achieve the final gait recognition. Experimental results show that this method has better identification accuracy than other comparative methods with the macro-F1 reaching 96.7%.

Download Full-text

Gait Recognition Analysis for Human Identification Analysis- A Hybrid Deep Learning Process

10.21203/rs.3.rs-549846/v1 ◽

2021 ◽

Author(s):

Mathivanan B ◽

Perumal P

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Feature Selection ◽

Median Filter ◽

Gait Recognition ◽

Human Gait ◽

Scale Invariant ◽

Inference System ◽

Statistical Measures ◽

Hu Moments

Abstract Gait is an individual biometric behavior which can be detected based on distance which has different submissions in social security, forensic detection and crime prevention. Hence, in this paper, Advanced Deep Belief Neural Network with Black Widow Optimization (ADBNN-BWO) Algorithm is developed to identify the human emotions by human walking style images. This proposed methodology is working based on four stages like pre-processing, feature extraction, feature selection and classification. For the pre-processing, contrast enhancement median filter is used and Hu Moments, GLCM, Fast Scale-invariant feature transform (F-SIFT), in addition skeleton features are used for the feature extraction. To extract the features efficiently, the feature extraction algorithm can be often very essential calculation. After that, feature selection is performed. Then the classification process is done by utilizing the proposed ADBNN-BWO Algorithm. Based on the proposed method, the human gait recognition is achieved which utilized to identify the emotions from the walking style. The proposed method is validated by using the open source gait databases. The proposed method is implemented in MATLAB platform and their corresponding performances/outputs are evaluated. Moreover, the statistical measures of proposed method are also determined and compared with the existing method as Artificial Neural Network (ANN), Mayfly algorithm with Particle Swarm Optimization (MA-PSO), Recurrent Neural Network -PSO (RNN-PSO) and Adaptive Neuro Fuzzy Inference System (ANFIS) respectively.

Download Full-text

Systematic Review on Human Gait Analysis using Deep Learning Models

10.54216/ajbor.060101 ◽

2021 ◽

pp. 09-22

Author(s):

Piyush Kumar Shukla ◽

◽

Prashant Kumar Shukla ◽

Keyword(s):

Deep Learning ◽

Gait Analysis ◽

Gait Recognition ◽

The Body ◽

Human Gait ◽

Learning Models ◽

Gait Patterns ◽

Learning Techniques ◽

The Individual ◽

Human Gait Analysis

Human Gait is known as a behavioral characteristic of humans, compared with the other biometrics gait is found to be a difficult process to conceal. Human gait analysis is usually done by extracting the features from the body. Analysis of gait involves evaluating the individual by means of kinematic analysis while walking along a surface. The main objective and the purpose of gait recognition is to give the best method where risks are recognized in places where there is a need for high security in any public place and to detect diseases like Parkinson’s. In order to acquire a normal person’s identification and validation performance, various Deep Learning techniques are totally studied and modeled the biometrics of gait which is based on walking data. It is reviewed that among various essential metrics that are used, deep learning convolution neural networks are typically better Machine Learning models. The main objective of the present study was to examine in detail individual gait patterns. Finally, this paper recommends deep learning methods and suggests the directions for future gait analysis and also for its applications.

Download Full-text

Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks

Sensors ◽

10.3390/s21175888 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5888

Author(s):

Joaquín Figueroa Barraza ◽

Enrique López Droguett ◽

Marcelo Ramos Martins

Keyword(s):

Neural Networks ◽

Feature Selection ◽

Deep Learning ◽

High Performance ◽

Deep Neural Networks ◽

Health Management ◽

Remaining Useful Life ◽

Health State ◽

Prognostics And Health Management ◽

Selection Framework

In the last five years, the inclusion of Deep Learning algorithms in prognostics and health management (PHM) has led to a performance increase in diagnostics, prognostics, and anomaly detection. However, the lack of interpretability of these models results in resistance towards their deployment. Deep Learning-based models fall within the accuracy/interpretability tradeoff, which means that their complexity leads to high performance levels but lacks interpretability. This work aims at addressing this tradeoff by proposing a technique for feature selection embedded in deep neural networks that uses a feature selection (FS) layer trained with the rest of the network to evaluate the input features’ importance. The importance values are used to determine which will be considered for deployment of a PHM model. For comparison with other techniques, this paper introduces a new metric called ranking quality score (RQS), that measures how performance evolves while following the corresponding ranking. The proposed framework is exemplified with three case studies involving health state diagnostics and prognostics and remaining useful life prediction. Results show that the proposed technique achieves higher RQS than the compared techniques, while maintaining the same performance level when compared to the same model but without an FS layer.

Download Full-text