Gait Recognition Using Principal Curves and Neural Networks

2006 ◽  
pp. 238-243 ◽  
Author(s):  
Han Su ◽  
Fenggang Huang
Keyword(s):  
Neural Networks ◽  
Gait Recognition ◽  
Principal Curves

Gait recognition using multichannel convolution neural networks

2019 ◽  
Vol 32 (18) ◽  
pp. 14275-14285 ◽  
Author(s):  
Xiuhui Wang ◽  
Jiajia Zhang ◽  
Wei Qi Yan
Keyword(s):  
Neural Networks ◽  
Gait Recognition ◽  
Convolution Neural Networks

scholarly journals IDNet: Smartphone-based gait recognition with convolutional neural networks

2018 ◽  
Vol 74 ◽  
pp. 25-37 ◽  
Author(s):  
Matteo Gadaleta ◽  
Michele Rossi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gait Recognition

Abnormal Gait Recognition in Real-Time using Recurrent Neural Networks

2020 ◽  
Author(s):  
Thanaporn Jinnovart ◽  
Xiongcai Cai ◽  
Kundjanasith Thonglek
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Recurrent Neural Networks ◽  
Gait Recognition ◽  
Abnormal Gait

scholarly journals GAIT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORKS

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 207-212 ◽  
Author(s):  
A. Sokolova ◽  
A. Konushin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Gait Recognition ◽  
Learning Approach ◽  
Network Architectures ◽  
Motion Information ◽  
Advantages And Disadvantages

In this work we investigate the problem of people recognition by their gait. For this task, we implement deep learning approach using the optical flow as the main source of motion information and combine neural feature extraction with the additional embedding of descriptors for representation improvement. In order to find the best heuristics, we compare several deep neural network architectures, learning and classification strategies. The experiments were made on two popular datasets for gait recognition, so we investigate their advantages and disadvantages and the transferability of considered methods.

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