Human Gait Recognition Using Gait Flow Image and Extension Neural Network

2015 ◽  
pp. 1-10 ◽  
Author(s):  
Parul Arora ◽  
Smriti Srivastava ◽  
Shivank
Keyword(s):  
Neural Network ◽  
Gait Recognition ◽  
Human Gait ◽  
Flow Image

scholarly journals Multimodal Few-Shot Learning for Gait Recognition

2020 ◽  
Vol 10 (21) ◽  
pp. 7619
Author(s):  
Jucheol Moon ◽  
Nhat Anh Le ◽  
Nelson Hebert Minaya ◽  
Sang-Il Choi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Gait Recognition ◽  
Gait Pattern ◽  
Recognition Problem ◽  
Human Gait ◽  
Training Dataset ◽  
Support Vector ◽  
Open Set ◽  
Latent Space

A person’s gait is a behavioral trait that is uniquely associated with each individual and can be used to recognize the person. As information about the human gait can be captured by wearable devices, a few studies have led to the proposal of methods to process gait information for identification purposes. Despite recent advances in gait recognition, an open set gait recognition problem presents challenges to current approaches. To address the open set gait recognition problem, a system should be able to deal with unseen subjects who have not included in the training dataset. In this paper, we propose a system that learns a mapping from a multimodal time series collected using insole to a latent (embedding vector) space to address the open set gait recognition problem. The distance between two embedding vectors in the latent space corresponds to the similarity between two multimodal time series. Using the characteristics of the human gait pattern, multimodal time series are sliced into unit steps. The system maps unit steps to embedding vectors using an ensemble consisting of a convolutional neural network and a recurrent neural network. To recognize each individual, the system learns a decision function using a one-class support vector machine from a few embedding vectors of the person in the latent space, then the system determines whether an unknown unit step is recognized as belonging to a known individual. Our experiments demonstrate that the proposed framework recognizes individuals with high accuracy regardless they have been registered or not. If we could have an environment in which all people would be wearing the insole, the framework would be used for user verification widely.

A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition

2020 ◽  
Author(s):  
Habiba Arshad ◽  
Muhammad Attique Khan ◽  
Muhammad Irfan Sharif ◽  
Mussarat Yasmin ◽  
João Manuel R. S. Tavares ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gait Recognition ◽  
Deep Convolutional Neural Network ◽  
Human Gait ◽  
Features Selection

scholarly journals Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5466 ◽  
Author(s):  
Xinrui Jiang ◽  
Ye Zhang ◽  
Qi Yang ◽  
Bin Deng ◽  
Hongqiang Wang
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Millimeter Wave ◽  
Gait Recognition ◽  
Three Dimensional ◽  
Human Gait ◽  
Radar Signal ◽  
Learning Technology ◽  
Motion Speed ◽  
Signal Processing Algorithms

At present, there are two obvious problems in radar-based gait recognition. First, the traditional radar frequency band is difficult to meet the requirements of fine identification with due to its low carrier frequency and limited micro-Doppler resolution. Another significant problem is that radar signal processing is relatively complex, and the existing signal processing algorithms are poor in real-time usability, robustness and universality. This paper focuses on the two basic problems of human gait detection with radar and proposes a human gait classification and recognition method based on millimeter-wave array radar. Based on deep-learning technology, a multi-channel three-dimensional convolution neural network is proposed on the basis of improving the residual network, which completes the classification and recognition of human gait through the hierarchical extraction and fusion of multi-dimensional features. Taking the three-dimensional coordinates, motion speed and intensity of strong scattering points in the process of target motion as network inputs, multi-channel convolution is used to extract motion features, and the classification and recognition of typical daily actions are completed. The experimental results show that we have more than 92.5% recognition accuracy for common gait categories such as jogging and normal walking.

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

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.

scholarly journals Individuality decoded by running patterns: Movement characteristics that determine the uniqueness of human running

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249657
Author(s):  
Fabian Hoitz ◽  
Vinzenz von Tscharner ◽  
Jennifer Baltich ◽  
Benno M. Nigg
Keyword(s):  
Neural Network ◽  
Sagittal Plane ◽  
Gait Recognition ◽  
Transverse Plane ◽  
Human Gait ◽  
Ground Reaction Forces ◽  
Movement Trajectories ◽  
Gait Characteristics ◽  
The Neural Network ◽  
Reaction Forces

Human gait is as unique to an individual as is their fingerprint. It remains unknown, however, what gait characteristics differentiate well between individuals that could define the uniqueness of human gait. The purpose of this work was to determine the gait characteristics that were most relevant for a neural network to identify individuals based on their running patterns. An artificial neural network was trained to recognize kinetic and kinematic movement trajectories of overground running from 50 healthy novice runners (males and females). Using layer-wise relevance propagation, the contribution of each variable to the classification result of the neural network was determined. It was found that gait characteristics of the coronal and transverse plane as well as medio-lateral ground reaction forces provided more information for subject identification than gait characteristics of the sagittal plane and ground reaction forces in vertical or anterior-posterior direction. Additionally, gait characteristics during the early stance were more relevant for gait recognition than those of the mid and late stance phase. It was concluded that the uniqueness of human gait is predominantly encoded in movements of the coronal and transverse plane during early stance.

Analysis of Gait Flow Image and Gait Gaussian Image Using Extension Neural Network for Gait Recognition

2020 ◽  
pp. 429-449 ◽  
Author(s):  
Parul Arora ◽  
Smriti Srivastava ◽  
Shivank Singhal
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Gait Recognition ◽  
Human Gait ◽  
Support Vector ◽  
Gaussian Image ◽  
Model Free ◽  
Flow Image ◽  
Flow Features ◽  
A New Technique

This paper proposes a new technique to recognize human gait by combining model free feature extraction approaches and a classifier. Gait flow image (GFI) and gait Gaussian image (GGI) are the two feature extraction techniques used in combination with ENN. GFI is a gait period based technique, uses optical flow features. So it directly focuses on dynamic part of human gait. GGI is another gait period based technique, computed by applying Gaussian membership function on human silhouettes. Next, ENN has been used as a classifier which combines the extension theory and neural networks. All the study has been done on CASIA-A and OU-ISIR treadmill B databases. The results derived using ENN are compared with SVM (support vector machines) and NN (Nearest neighbor) classifiers. ENN proved to give good accuracy and less iteration as compared to other traditional methods.

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