Lee, H.S.[Hee-Sung],
Hong, S.J.[Sung-Jun],
Kim, E.T.[Eun-Tai],
An efficient gait recognition based on a selective neural network
ensemble,
IJIST(18), No. 4, 2008, pp. 237-241.
DOI Link
0810
BibRef
Hao, Z.F.[Zhi-Feng],
He, L.F.[Li-Fang],
Chen, B.Q.[Bing-Qian],
Yang, X.W.[Xiao-Wei],
A Linear Support Higher-Order Tensor Machine for Classification,
IP(22), No. 7, 2013, pp. 2911-2920.
IEEE DOI
1307
radial basis function networks; higher-order tensors;
third-order gait recognition
BibRef
Hong, S.J.[Sung-Jun],
Lee, H.S.[Hee-Sung],
Kim, E.T.[Eun-Tai],
Probabilistic gait modelling and recognition,
IET-CV(7), No. 1, 2013, pp. 56-70.
DOI Link
1307
Award, IET CV Premium.
BibRef
Takemura, N.[Noriko],
Makihara, Y.S.[Yasu-Shi],
Muramatsu, D.[Daigo],
Echigo, T.[Tomio],
Yagi, Y.S.[Yasu-Shi],
On Input/Output Architectures for Convolutional Neural Network-Based
Cross-View Gait Recognition,
CirSysVideo(29), No. 9, September 2019, pp. 2708-2719.
IEEE DOI
1909
Gait recognition, Probes, Network architecture, Robustness,
Performance evaluation, Neural networks,
gait recognition
BibRef
Makihara, Y.S.[Yasu-Shi],
Adachi, D.,
Xu, C.,
Yagi, Y.S.[Yasu-Shi],
Gait Recognition by Deformable Registration,
Biometrics18(674-67410)
IEEE DOI
1812
Strain, Gait recognition, Probes, Deformable models, Measurement,
Shape, Computational modeling
BibRef
Sagawa, R.[Ryusuke],
Makihara, Y.S.[Yasu-Shi],
Echigo, T.[Tomio],
Yagi, Y.S.[Yasu-Shi],
Matching Gait Image Sequences in the Frequency Domain for Tracking
People at a Distance,
ACCV06(II:141-150).
Springer DOI
0601
BibRef
Akae, N.[Naoki],
Makihara, Y.S.[Yasu-Shi],
Yagi, Y.S.[Yasu-Shi],
The optimal camera arrangement by a performance model for gait
recognition,
FG11(292-297).
IEEE DOI
1103
BibRef
Mansur, A.[Al],
Makihara, Y.S.[Yasu-Shi],
Aqmar, R.[Rasyid],
Yagi, Y.S.[Yasu-Shi],
Gait Recognition under Speed Transition,
CVPR14(2521-2528)
IEEE DOI
1409
BibRef
Alotaibi, M.[Munif],
Mahmood, A.[Ausif],
Improved gait recognition based on specialized deep convolutional
neural network,
CVIU(164), No. 1, 2017, pp. 103-110.
Elsevier DOI
1801
BibRef
Earlier:
Improved Gait recognition based on specialized deep convolutional
neural networks,
AIPR15(1-7)
IEEE DOI
1605
Convolutional neural networks
BibRef
Alotaibi, M.[Munif],
Mahmood, A.[Ausif],
Reducing covariate factors of gait recognition using feature selection
and dictionary-based sparse coding,
SIViP(11), No. 6, September 2017, pp. 1131-1138.
WWW Link.
1708
biometrics (access control)
BibRef
Wu, Z.F.[Zi-Feng],
Huang, Y.Z.[Yong-Zhen],
Wang, L.[Liang],
Wang, X.G.[Xiao-Gang],
Tan, T.N.[Tie-Niu],
A Comprehensive Study on Cross-View Gait Based Human Identification
with Deep CNNs,
PAMI(39), No. 2, February 2017, pp. 209-226.
IEEE DOI
1702
BibRef
Gadaleta, M.[Matteo],
Rossi, M.[Michele],
IDNet: Smartphone-based gait recognition with convolutional neural
networks,
PR(74), No. 1, 2018, pp. 25-37.
Elsevier DOI
1711
Biometric, gait, analysis
BibRef
Wu, H.[Huimin],
Weng, J.[Jian],
Chen, X.[Xin],
Lu, W.[Wei],
Feedback weight convolutional neural network for gait recognition,
JVCIR(55), 2018, pp. 424-432.
Elsevier DOI
1809
Gait recognition, Deep learning, Convolutional neural network,
Weighted receptive field
BibRef
Sokolova, A.[Anna],
Konushin, A.[Anton],
Pose-based deep gait recognition,
IET-Bio(8), No. 2, March 2019, pp. 134-143.
DOI Link
1902
BibRef
Yu, S.Q.[Shi-Qi],
Liao, R.J.[Ri-Jun],
An, W.Z.[Wei-Zhi],
Chen, H.F.[Hai-Feng],
García, E.B.[Edel B.],
Huang, Y.Z.[Yong-Zhen],
Poh, N.[Norman],
GaitGANv2: Invariant gait feature extraction using generative
adversarial networks,
PR(87), 2019, pp. 179-189.
Elsevier DOI
1812
BibRef
Earlier: A1, A4, A5, A7, Only:
GaitGAN: Invariant Gait Feature Extraction Using Generative
Adversarial Networks,
Biometrics17(532-539)
IEEE DOI
1709
Gait recognition, Generative adversarial networks, Invariant feature.
Clothing, Feature extraction,
Generators, Legged locomotion, Training
BibRef
Lamar Leon, J.[Javier],
Alonso-Baryolo, R.,
García-Reyes, E.B.[Edel B.],
Gonzalez-Diaz, R.[Rocio],
Persistent homology-based gait recognition robust to upper body
variations,
ICPR16(1083-1088)
IEEE DOI
1705
Feature extraction, Gait recognition, Legged locomotion,
Pattern recognition, Robustness, Shape, Video, sequences
BibRef
Lamar Leon, J.[Javier],
Cerri, A.[Andrea],
García-Reyes, E.B.[Edel B.],
Gonzalez-Diaz, R.[Rocio],
Gait-Based Gender Classification Using Persistent Homology,
CIARP13(II:366-373).
Springer DOI
1311
BibRef
Xu, Z.P.[Zhao-Peng],
Lu, W.[Wei],
Zhang, Q.[Qin],
Yeung, Y.L.[Yui-Leong],
Chen, X.[Xin],
Gait recognition based on capsule network,
JVCIR(59), 2019, pp. 159-167.
Elsevier DOI
1903
Gait recognition, Capsule network, Deep learning
BibRef
Battistone, F.[Francesco],
Petrosino, A.[Alfredo],
TGLSTM: A time based graph deep learning approach to gait recognition,
PRL(126), 2019, pp. 132-138.
Elsevier DOI
1909
Gait, Action
BibRef
Zhang, Y.,
Huang, Y.,
Yu, S.,
Wang, L.,
Cross-View Gait Recognition by Discriminative Feature Learning,
IP(29), No. , 2020, pp. 1001-1015.
IEEE DOI
1911
Gait recognition, Feature extraction,
Generative adversarial networks, Face recognition, Deep learning,
spatial-temporal features
BibRef
Hu, K.[Kun],
Wang, Z.Y.[Zhi-Yong],
Wang, W.[Wei],
Ehgoetz Martens, K.A.[Kaylena A.],
Wang, L.[Liang],
Tan, T.N.[Tie-Niu],
Lewis, S.J.G.[Simon J. G.],
Feng, D.D.[David Dagan],
Graph Sequence Recurrent Neural Network for Vision-Based Freezing of
Gait Detection,
IP(29), No. 1, 2020, pp. 1890-1901.
IEEE DOI
1912
Videos, Deep learning, Recurrent neural networks, Task analysis,
Feature extraction, Legged locomotion, Parkinson's disease,
graph sequence
BibRef
Hu, K.[Kun],
Wang, Z.Y.[Zhi-Yong],
Martens, K.E.[Kaylena Ehgoetz],
Lewis, S.[Simon],
Vision-Based Freezing of Gait Detection with Anatomic Patch Based
Representation,
ACCV18(I:564-576).
Springer DOI
1906
BibRef
Singh, J.[Jaiteg],
Goyal, G.[Gaurav],
Identifying biometrics in the wild: A time, erosion and neural
inspired framework for gait identification,
JVCIR(66), 2020, pp. 102725.
Elsevier DOI
2003
Convolutional neural network, OU-ISIR, CASIA, Erosion, Silhouette
BibRef
Babaee, M.,
Zhu, Y.,
Köpüklü, O.,
Hörmann, S.,
Rigoll, G.,
Gait Energy Image Restoration Using Generative Adversarial Networks,
ICIP19(2596-2600)
IEEE DOI
1910
Gait Recognition, Gait Energy Image, Generative Adversarial Networks
BibRef
Babaee, M.,
Rigoll, G.,
View-Invariant Gait Representation Using Joint Bayesian Regularized
Non-negative Matrix Factorization,
HumID17(2583-2589)
IEEE DOI
1802
Bayes methods, Cameras, Gaussian distribution, Linear programming,
Mathematical model, Principal component analysis
BibRef
Hofmann, M.[Martin],
Rigoll, G.[Gerhard],
Improved Gait Recognition using Gradient Histogram Energy Image,
ICIP12(1389-1392).
IEEE DOI
1302
BibRef
Han, T.,
Xing, X.,
Wu, Y.N.,
Learning Multi-view Generator Network for Shared Representation,
ICPR18(2062-2068)
IEEE DOI
1812
Generators, Task analysis, Training, Image generation,
Gait recognition, Learning systems, Fuses, Multi-view learning,
BibRef
Xu, W.C.[Wen-Chao],
Pang, Y.X.[Yu-Xin],
Yang, Y.Q.[Yan-Qin],
Liu, Y.B.[Yan-Bo],
Human Activity Recognition Based On Convolutional Neural Network,
ICPR18(165-170)
IEEE DOI
1812
Convolution, Activity recognition, Accelerometers, Training,
Feature extraction, Support vector machines, Legged locomotion,
SVM
BibRef
Sokolova, A.,
Konushin, A.,
Gait Recognition Based On Convolutional Neural Networks,
PTVSBB17(207-212).
DOI Link
1805
BibRef
Charalambous, C.[Christoforos],
Bharath, A.[Anil],
A data augmentation methodology for training machine/deep learning gait
recognition algorithms,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Rustagi, L.[Luv],
Kumar, L.[Lokendra],
Pillai, G.N.,
Human Gait Recognition Based on Dynamic and Static Features Using
Generalized Regression Neural Network,
ICMV09(64-68).
IEEE DOI
0912
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Walking, Gait Recognition, University of Southampton .