16.7.4.5.1 Walking, Gait Recognition, Neural Networks, CNN

Chapter Contents (Back)
Gait. Neural Networks.

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


Sakai, A.[Akinari], Sogi, N.[Naoya], Fukui, K.[Kazuhiro],
Gait Recognition Based on Constrained Mutual Subspace Method with CNN Features,
MVA19(1-6)
DOI Link 1911
convolutional neural nets, feature extraction, gait analysis, image sequences, gait recognition, Principal component analysis 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 .


Last update:Jun 29, 2020 at 10:24:28