Gait recognition based on Gabor wavelets and (2D)2PCA

Wang, Xiuhui; Wang, Jun; Yan, Ke

doi:10.1007/s11042-017-4903-7

Cited by 41 publications

(28 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Approaches of gait recognition based on holistic features [3][4][5][6][7][8] directly utilise the geometric information of human walking from image sequences and generally have higher computational cost. In [3], an arbitrary view transformation model (VTM) is designed, which can match a pair of gait features from any views in theory.…”

Section: Related Workmentioning

confidence: 99%

“…The input and output architectures were investigated in the work [6] for convolutional neural network-based cross-view gait recognition, which discussed verification versus identification and the trade-off between spatial displacement of different subjects and views. Furthermore, a Gabor wavelets-based gait recognition algorithm was proposed [7], which employs two-dimensional principal component analysis 2D 2 PCA method to reduce the feature space dimension. Islam et al [8] presented a wavelet-based feature extraction method for gait recognition, and a template matchingbased approach is used in the classification process.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Multi‐perspective gait recognition based on classifier fusion

Wang

Feng

2019

IET Image Processing

Self Cite

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multi‐perspective gait recognition based on classifier fusion

Wang

Feng

2019

IET Image Processing

Self Cite

View full text Add to dashboard Cite

“…Lishani et al [79] extracted features from GEIs using a bank of Gabor filters, then the resulting features were combined with spectral regression Kernel discriminant analysis (SRKDA). Wang et al [80] projected the GEI Gabor wavelet features in a new subspace using 2DPCA. Ma et al [81] tried to find a discriminative low-dimensional subspace based on tensor subspace ensemble learning totally corrective boosting (TSEL_TCB) as well as its kerneled version KSEL_TCB [82].…”

Section: Model-free Gait Recognitionmentioning

confidence: 99%

“…Wang et al . [80] projected the GEI Gabor wavelet features in a new subspace using 2DPCA. Ma et al .…”

Section: Model‐free Gait Recognitionmentioning

confidence: 99%

Robust gait recognition: a comprehensive survey

2018

View full text Add to dashboard Cite

Gait recognition has emerged as an attractive biometric technology for the identification of people by analysing the way they walk. However, one of the main challenges of the technology is to address the effects of inherent various intra-class variations caused by covariate factors such as clothing, carrying conditions, and view angle that adversely affect the recognition performance. The main aim of this survey is to provide a comprehensive overview of existing robust gait recognition methods. This is intended to provide researchers with state of the art approaches in order to help advance the research topic through an understanding of basic taxonomies, comparisons, and summaries of the state-of-the-art performances on several widely used gait recognition datasets.

show abstract

“…Wang et al [16] innovatively applied genetic algorithms to the recognition of finger and arm movement direction. The main research object of Wang et al [17] was the body; they tracked the trajectory of the human body and achieved gait recognition. In order to construct a virtual traffic interaction environment, we wanted to simulate traffic police command gestures, so the motion trajectory of the human arm was determined as the main research object.…”

Section: Related Workmentioning

confidence: 99%

Traffic Command Gesture Recognition for Virtual Urban Scenes Based on a Spatiotemporal Convolution Neural Network

Zhang

Wang

et al. 2018

IJGI

View full text Add to dashboard Cite

Intelligent recognition of traffic police command gestures increases authenticity and interactivity in virtual urban scenes. To actualize real-time traffic gesture recognition, a novel spatiotemporal convolution neural network (ST-CNN) model is presented. We utilized Kinect 2.0 to construct a traffic police command gesture skeleton (TPCGS) dataset collected from 10 volunteers. Subsequently, convolution operations on the locational change of each skeletal point were performed to extract temporal features, analyze the relative positions of skeletal points, and extract spatial features. After temporal and spatial features based on the three-dimensional positional information of traffic police skeleton points were extracted, the ST-CNN model classified positional information into eight types of Chinese traffic police gestures. The test accuracy of the ST-CNN model was 96.67%. In addition, a virtual urban traffic scene in which real-time command tests were carried out was set up, and a real-time test accuracy rate of 93.0% was achieved. The proposed ST-CNN model ensured a high level of accuracy and robustness. The ST-CNN model recognized traffic command gestures, and such recognition was found to control vehicles in virtual traffic environments, which enriches the interactive mode of the virtual city scene. Traffic command gesture recognition contributes to smart city construction.

show abstract

Gait recognition based on Gabor wavelets and (2D)2PCA

Cited by 41 publications

References 32 publications

Multi‐perspective gait recognition based on classifier fusion

Multi‐perspective gait recognition based on classifier fusion

Robust gait recognition: a comprehensive survey

Traffic Command Gesture Recognition for Virtual Urban Scenes Based on a Spatiotemporal Convolution Neural Network

Contact Info

Product

Resources

About