Cited by 72Hand Gesture Recognition SystemsContext-Aware Activity Recognition SystemsGaze Tracking and Assistive Technology

Deep Fisher discriminant learning for mobile hand gesture recognition

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Li, Ce, Xie, Chunyu, Zhang, Baochang, Chen, Chen, Han, Jungong·2018·Pattern Recognition

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  1. C. Hong, J. Yu, D. Tao, M. Wang, Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval, IEEE Trans- actions on Industrial Electronics 62 (6) (2015) 3742-3751.
  2. J. Yu, C. Hong, Y. Rui, D. Tao, Multi-task autoencoder model for recov- 455 ering human poses, IEEE Transactions on Industrial Electronics PP (99) (2017) 1-1.
  3. H. Gjoreski, J. Bizjak, M. Gjoreski, M. Gams, Comparing deep and clas- sical machine learning methods for human activity recognition using wrist accelerometer, in: Proceedings of the 25th International Joint Conference on Artificial Intelligence, New York, 2016, pp. 1-7.
  4. J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Empirical evaluation of gated 565 recurrent neural networks on sequence modeling, Eprint arXiv.
  5. L. V. D. Maaten, E. o. Postma, H. J. V. D. Herik, Dimensionality reduc- tion: A comparative review, IEEE Transactions on Pattern Analysis and 570 Machine Intelligence 10.

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Authors
Li, C., Xie, C., Zhang, B., Chen, C., & Han, J.
Affiliations
  • China University of Mining and Technology - Beijing (CN)
  • Beihang University (CN)
  • University of Central Florida (US)
  • Lancaster University (GB)
Corresponding author
Zhang, Baochang
Venue
Pattern Recognition
Publisher
Elsevier BV
Published
2018-05
Volume
77
Pages
276-288
ISSN
0031-3203 (ISSN-L)
OpenAlex
W2963935481
Cited by
72
References
75
Citations by year
2026: 2 · 2025: 4 · 2024: 6 · 2023: 5 · 2022: 12 · 2021: 10 · 2020: 12 · 2019: 17 · 2018: 3 · 2016: 1
FWCI
10.48
Type
journal article
Language
en
Open access
Locations
  • Pattern Recognition (publishedVersion)
  • Lancaster EPrints (Lancaster University) (submittedVersion)
  • Journal of International Crisis and Risk Communication Research (submittedVersion)
Funders
  • Ministry of Education of the People's Republic of China
  • National Laboratory of Pattern Recognition
  • Royal Society
  • Shenzhen Peacock Plan
  • National Natural Science Foundation of China (61473086, 61601466, 61672079)
Topics
  • Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
Keywords
hand gesture recognition, mobile devices, linear discriminant analysis, bidirectional LSTM, bidirectional GRU, user interaction, biometrics

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