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Compared to other biometrics, gait is difficult to conceal and has the advantage of being unobtrusive.
G. Lin, A. Milan, C. Shen, and I. Reid, “Refinenet: Multi-path refinement networks for high-resolution semantic segmentation,” in CVPR , 2017, pp. 1925–1934
1934
Earlier work this paper cites.
M. D. Addlesee, A. Jones, F. Livesey, and F. Samaria, “The orl active floor [sensor system],” IEEE Personal Communications , vol. 4, no. 5, pp. 35–41, 1997
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
R. E. Mayagoitia, A. V. Nene, and P. H. Veltink, “Accelerometer and rate gyroscope measurement of kinematics: an inexpensive alternative to optical motion analysis systems,” Journal of biomechanics , vol. 35, no. 4, pp. 537–542, 2002
2002
Earlier work this paper cites.
L. Lee and W. E. L. Grimson, “Gait analysis for recognition and classification,” in AFGR , 2002, pp. 155–162
2002
Earlier work this paper cites.
C. Nakajima, M. Pontil, B. Heisele, and T. Poggio, “Full-body person recognition system,” Pattern Recognition , vol. 36, no. 9, pp. 1997–2006, 2003
2003
Earlier work this paper cites.
J. Mäntyjärvi, M. Lindholm, E. Vildjiounaite, S.-M. Mäkelä, and H. Ailisto, “Identifying users of portable devices from gait pattern with accelerometers.” in ICASSP , 2005, pp. 973–976
2005
Earlier work this paper cites.
H. J. Ailisto, M. Lindholm, J. Mantyjarvi, E. Vildjiounaite, and S.-M. Makela, “Identifying people from gait pattern with accelerometers,” in BTHI , 2005
2005
Earlier work this paper cites.
M. Nixon, T. Tan, and R. Chellapppa, “Human identification based on gait,” Springer Science + Business Media Inc. , 2006, ch. 1
2006
Earlier work this paper cites.
Y. Makihara, R. Sagawa, Y. Mukaigawa, T. Echigo, and Y. Yagi, “Gait recognition using a view transformation model in the frequency domain,” in ECCV , 2006, pp. 151–163
2006
Earlier work this paper cites.
J. A. Ward, P. Lukowicz, G. Troster, and T. E. Starner, “Activity recognition of assembly tasks using body-worn microphones and accelerometers,” IEEE T-PAMI , vol. 28, no. 10, pp. 1553–1567, 2006
2006
Earlier work this paper cites.
D. Gafurov, K. Helkala, and T. Søndrol, “Biometric gait authentication using accelerometer sensor,” Journal of Computers , vol. 1, no. 7, pp. 51–59, 2006
2006
Earlier work this paper cites.
J. Man and B. Bhanu, “Individual recognition using gait energy image,” IEEE T-PAMI , vol. 28, no. 2, pp. 316–322, 2006
2006
Earlier work this paper cites.
B. Huang, M. Chen, P. Huang, and Y. Xu, “Gait modeling for human identification,” in ICRA , 2007, pp. 4833–4838
2007
Earlier work this paper cites.
D. Gafurov, E. Snekkenes, and P. Bours, “Spoof attacks on gait authentication system,” IEEE T-IFS , vol. 2, no. 3, pp. 491–502, 2007
2007
Earlier work this paper cites.
——, “Gait authentication and identification using wearable accelerometer sensor,” in AIATW , 2007, pp. 220–225
2007
Earlier work this paper cites.
R. Liu, Z. Duan, J. Zhou, and M. Liu, “Identification of individual walking patterns using gait acceleration,” in ICBBE , 2007
2007
Earlier work this paper cites.
R. Liu, J. Zhou, M. Liu, and X. Hou, “A wearable acceleration sensor system for gait recognition,” in ICIEA , 2007, pp. 2654–2659
2007
Earlier work this paper cites.
J.-Y. Yang, J.-S. Wang, and Y.-P. Chen, “Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers,” Pattern Recognition Letters , vol. 29, no. 16, pp. 2213–2220, 2008
2008
Earlier work this paper cites.
L. v. d. Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of machine learning research , vol. 9, pp. 2579–2605, 2008
2008
Earlier work this paper cites.
D. Gafurov and E. Snekkenes, “Gait recognition using wearable motion recording sensors,” EURASIP Journal on Advances in Signal Processing , vol. 2009, p. 7, 2009
2009
Earlier work this paper cites.
J. Zhang, J. Pu, C. Chen, and R. Fleischer, “Low-resolution gait recognition,” IEEE Trans. on SMC, Part B: Cybernetics , vol. 40, no. 4, pp. 986–996, 2010
2010
Earlier work this paper cites.
J. Kwapisz, G. Weiss, and S. Moore, “Cell phone-based biometric identification,” in BTAS , 2010, pp. 1–7
2010
Earlier work this paper cites.
J. Frank, S. Mannor, and D. Precup, “Activity and gait recognition with time-delay embeddings.” in AAAI , 2010
2010
Earlier work this paper cites.
G. Trivino, A. Alvarez-Alvarez, and G. Bailador, “Application of the computational theory of perceptions to human gait pattern recognition,” Pattern Recognition , vol. 43, no. 7, pp. 2572–2581, 2010
2010
Earlier work this paper cites.
M. Derawi, P. Bours, and K. Holien, “Improved cycle detection for accelerometer based gait authentication,” in IIHMSP , 2010
2010
Earlier work this paper cites.
R. Tronci, D. Muntoni, G. Fadda, M. Pili, N. Sirena, G. Murgia, M. Ristori, S. Ricerche, and F. Roli, “Fusion of multiple clues for photo-attack detection in face recognition systems,” in IJCB , 2011
2011
Earlier work this paper cites.
T. Plötz, N. Y. Hammerla, and P. Olivier, “Feature learning for activity recognition in ubiquitous computing,” in IJCAI , 2011
2011
Earlier work this paper cites.
N. Trung, Y. Makihara, H. Nagahara, R. Sagawa, Y. Mukaigawa, and Y. Yagi, “Phase registration in a gallery improving gait authentication,” in IJCB , 2011
2011
Earlier work this paper cites.
F. Xu, C. Bhagavatula, A. Jaech, U. Prasad, and M. Savvides, “Gait-ID on the move: Pace independent human identification using cell phone accelerometer dynamics,” in BTAS , 2012, pp. 8–15
2012
Earlier work this paper cites.
N. Trung, Y. Makihara, H. Nagahara, Y. Mukaigawa, and Y. Yagi, “Performance evaluation of gait recognition using the largest inertial sensor-based gait database,” in IAPR ICB , 2012, pp. 360–366
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in NIPS , 2012
2012
Earlier work this paper cites.
H. Iwama, M. Okumura, Y. Makihara, and Y. Yagi, “The ou-isir gait database comprising the large population dataset and performance evaluation of gait recognition,” IEEE T-IFS , vol. 7, no. 5, pp. 1511–1521, 2012
2012
Cited alongside, same era.
S. Kim, S. Yu, K. Kim, Y. Ban, and S. Lee, “Face liveness detection using variable focusing,” in ICB , 2013, pp. 1–6
2013
Cited alongside, same era.
A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in ICASSP , 2013, pp. 6645–6649
2013
Cited alongside, same era.
S. Ji, W. Xu, M. Yang, and K. Yu, “3d convolutional neural networks for human action recognition,” IEEE T-PAMI , vol. 35, no. 1, pp. 221–231, 2013
2013
Cited alongside, same era.
B. Sun, Y. Wang, and J. Banda, “Gait characteristic analysis and identification based on the iphone¡¯s accelerometer and gyrometer,” Sensors , vol. 14, no. 9, pp. 17 037–17 054, 2014
S. Yao, S. Hu, Y. Zhao, A. Zhang, and T. Abdelzaher, “Deepsense: A unified deep learning framework for time-series mobile sensing data processing,” in WWW , 2017, pp. 351–360
2017
Later among the works it cites.
H. Abujrida, E. Agu, and K. Pahlavan, “Smartphone-based gait assessment to infer parkinson’s disease severity using crowdsourced data,” in HI-POCT , 2017, pp. 208–211
2017
Later among the works it cites.
M. Muaaz and R. Mayrhofer, “Smartphone-based gait recognition: From authentication to imitation,” IEEE T-MC , vol. 16, no. 11, pp. 3209–3221, 2017
2017
Later among the works it cites.
A. Ferreira, G. Santos, A. Rocha, and S. Goldenstein, “User-centric coordinates for applications leveraging 3-axis accelerometer data,” IEEE Sensors Journal , vol. 17, no. 16, pp. 5231–5243, 2017
2017
Later among the works it cites.
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2014
Cited alongside, same era.
T. T. Ngo, Y. Makihara, H. Nagahara, Y. Mukaigawa, and Y. Yagi, “Orientation-compensative signal registration for owner authentication using an accelerometer,” IEICE Transactions on Information and Systems , vol. 97, no. 3, pp. 541–553, 2014
2014
Cited alongside, same era.
T. Ngo, Y. Makihara, H. Nagahara, Y. Mukaigawa, and Y. Yagi, “The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication,” Pattern Recognition , vol. 47, no. 1, pp. 228–237, 2014
2014
Cited alongside, same era.
Y. Zhong and Y. Deng, “Sensor orientation invariant mobile gait biometrics,” in IJCB , 2014, pp. 1–8
2014
Cited alongside, same era.
J. Juen, Q. Cheng, V. Prieto-Centurion, J. A. Krishnan, and B. Schatz, “Health monitors for chronic disease by gait analysis with mobile phones,” Telemedicine and e-Health , vol. 20, no. 11, pp. 1035–1041, 2014
2014
Cited alongside, same era.
J. Lu, G. Wang, and P. Moulin, “Human identity and gender recognition from gait sequences with arbitrary walking directions,” IEEE T-IFS , vol. 9, no. 1, pp. 51–61, 2014
2014
Cited alongside, same era.
P. Chattopadhyay, S. Sural, and J. Mukherjee, “Frontal gait recognition from incomplete sequences using rgb-d camera,” IEEE T-IFS , vol. 9, no. 11, pp. 1843–1856, 2014
2014
Cited alongside, same era.
M. Zeng, L. T. Nguyen, B. Yu, O. J. Mengshoel, J. Zhu, P. Wu, and J. Zhang, “Convolutional neural networks for human activity recognition using mobile sensors,” in MCAS , 2014, pp. 197–205
2014
Cited alongside, same era.
2017
Later among the works it cites.
M. Alotaibi and A. Mahmood, “Improved gait recognition based on specialized deep convolutional neural network,” CVIU , vol. 164, pp. 103–110, 2017
2017
Later among the works it cites.
N. Takemura, Y. Makihara, D. Muramatsu, T. Echigo, and Y. Yagi, “On input/output architectures for convolutional neural network-based cross-view gait recognition,” IEEE Transactions on Circuits and Systems for Video Technology , 2017
2017
Later among the works it cites.
Y. Zhao and S. Zhou, “Wearable device-based gait recognition using angle embedded gait dynamic images and a convolutional neural network,” Sensors , vol. 17, no. 3, p. 478, 2017
2017
Later among the works it cites.
C. Li, X. Min, S. Sun, W. Lin, and Z. Tang, “Deepgait: a learning deep convolutional representation for view-invariant gait recognition using joint bayesian,” Applied Sciences , vol. 7, no. 3, p. 210, 2017
2017
Later among the works it cites.
F. M. Castro, M. J. Marín-Jiménez, N. Guil, and N. P. de la Blanca, “Automatic learning of gait signatures for people identification,” in ICANN , 2017, pp. 257–270
2017
Later among the works it cites.
S. Yu, H. Chen, E. B. G. Reyes, and P. Norman, “Gaitgan: invariant gait feature extraction using generative adversarial networks,” in CVPRW , 2017, pp. 30–37
2017
Later among the works it cites.
I. Rida, S. Al-maadeed et al. , “Robust gait recognition: a comprehensive survey,” IET Biometrics , 2018
2018
Closest in time.
W. An, R. Liao, S. Yu, Y. Huang, and P. C. Yuen, “Improving gait recognition with 3d pose estimation,” in CCBR , 2018, pp. 137–147
2018
Closest in time.
Q. Zou, L. Ni, Q. Wang, Q. Li, and S. Wang, “Robust gait recognition by integrating inertial and rgbd sensors,” IEEE Transactions on Cybernetics , vol. 48, no. 4, pp. 1136–1150, 2018
2018
Closest in time.
C. Shen, Y. Li, Y. Chen, X. Guan, and R. A. Maxion, “Performance analysis of multi-motion sensor behavior for active smartphone authentication,” IEEE T-IFS , vol. 13, no. 1, pp. 48–62, 2018
2018
Closest in time.
H. Zhao, Z. Wang, S. Qiu, Y. Shen, L. Zhang, K. Tang, and G. Fortino, “Heading drift reduction for foot-mounted inertial navigation system via multi-sensor fusion and dual-gait analysis,” IEEE Sensors Journal , 2018
2018
Closest in time.
W. Yuan and L. Zhang, “Gait classification and identity authentication using cnn,” in Asian Simulation Conference , 2018, pp. 119–128
2018
Closest in time.
M. Gadaleta and M. Rossi, “Idnet: Smartphone-based gait recognition with convolutional neural networks,” Pattern Recognition , vol. 74, pp. 25–37, 2018
2018
Closest in time.
2019
Closest in time.
Z. Zhang, L. Tran, X. Yin, Y. Atoum, X. Liu, J. Wan, and N. Wang, “Gait recognition via disentangled representation learning,” in CVPR , 2019, pp. 4710–4719
2019
Closest in time.
Y. Zhang, Y. Huang, L. Wang, and S. Yu, “A comprehensive study on gait biometrics using a joint cnn-based method,” Pattern Recognition , vol. 93, pp. 228–236, 2019
2019
Closest in time.
M. Zhou, Q. Wang, J. Yang, Q. Li, P. Jiang, Y. Chen, and Z. Wang, “Stealing your android patterns via acoustic signals,” IEEE T-MC , 2019
2019
Closest in time.
Q. Zou, Z. Zhang, Q. Li, X. Qi, Q. Wang, and S. Wang, “Deepcrack: Learning hierarchical convolutional features for crack detection,” IEEE Transactions on Image Processing , vol. 28, no. 3, pp. 1498–1512, 2019
2019
Closest in time.
L. Chen, W. Zhan, W. Tian, Y. He, and Q. Zou, “Deep integration: A multi-label architecture for road scene recognition,” IEEE Transactions on Image Processing , vol. 28, no. 10, pp. 4883–4898, 2019
2019
Closest in time.
Z. Shao, L. Wang, Z. Wang, W. Du, and W. Wu, “Saliency-aware convolution neural network for ship detection in surveillance video,” IEEE T-SCVT , 2019
2019
Closest in time.
Z. Shao, Y. Pan, C. Diao, and J. Cai, “Cloud detection in remote sensing images based on multiscale features-convolutional neural network,” IEEE Transactions on Geoscience and Remote Sensing , vol. 57, no. 6, pp. 4062–4076, 2019
2019
Closest in time.
L. Wu, J. Yang, M. Zhou, Y. Chen, and Q. Wang, “LVID: a multimodal biometrics authentication system on smartphones,” IEEE T-IFS , 2019
2019
Closest in time.
K. Ren, Q. Wang, C. Wang, Z. Qin, and X. Lin, “The security of autonomous driving: Threats, defences, and future directions,” Proceedings of the IEEE , pp. 1–14, 2019
2019
Closest in time.
C. Shen, Y. Chen, X. Guan, and R. A. Maxion, “Pattern-growth based mining mouse-interaction behavior for an active user authentication system,” IEEE Transactions on Dependable and Secure Computing , vol. 17, no. 2, pp. 335–349, 2020
2020
Closest in time.
Z. Zhang, Q. Zou, Y. Lin, L. Chen, and S. Wang, “Improved deep hashing with soft pairwise similarity for multi-label image retrieval,” IEEE Transactions on Multimedia , vol. 22, no. 2, pp. 540–553, 2020
2020
Closest in time.
Q. Zou, H. Jiang, Q. Dai, Y. Yue, L. Chen, and Q. Wang, “Robust lane detection from continuous driving scenes using deep neural networks,” IEEE Transactions on Vehicular Technology , vol. 69, no. 1, pp. 41–54, 2020
2020
Closest in time.
L. Zhao, Q. Wang, Q. Zou, Y. Zhang, and Y. Chen, “Privacy-preserving collaborative deep learning with unreliable participants,” IEEE T-IFS , vol. 15, no. 1, pp. 1486–1500, 2020
2020
Closest in time.