Lejun Yu
Impact in
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- Emotion and Mood Recognition
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- Face and Expression Recognition
- Face recognition and analysis
- Human Pose and Action Recognition
Papers in
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- Human Pose and Action Recognition 11
- Face recognition and analysis 10
- Face and Expression Recognition 8
- Advanced Image Processing Techniques 6
- Multimodal Machine Learning Applications 6
- Video Surveillance and Tracking Methods 6
- Advanced Vision and Imaging 5
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- Emotion and Mood Recognition 21
- Co-authors
- Jun He (42 shared papers)Bo Sun (38 shared papers)Chen‐Guang Liu (4 shared papers)Zhe Chi (5 shared papers)Fei Jiang (2 shared papers)Mazhar Ali Raja (2 shared papers)Chenguang Liu (2 shared papers)Ziyu Shao (1 shared paper)
In The Last Decade
Lejun Yu
56 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 129
- Experimental and Cognitive Psychology 334
- Computer Vision and Pattern Recognition 332
- Biomaterials 175
- Aquatic Science 83
- Human-Computer Interaction 69
Countries citing papers authored by Lejun Yu
This map shows the geographic impact of Lejun Yu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lejun Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lejun Yu more than expected).
Fields of papers citing papers by Lejun Yu
This network shows the impact of papers produced by Lejun Yu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lejun Yu. The network helps show where Lejun Yu may publish in the future.
Co-authors
The 25 scholars most cited alongside Lejun Yu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 59 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 157 | |
| 2 | 2017 | 111 | |
| 3 | 2020 | 79 | |
| 4 | 2017 | 59 | |
| 5 | 2018 | 46 | |
| 6 | 2016 | 45 | |
| 7 | 2021 | 44 | |
| 8 | 2007 | 43 | |
| 9 | 2015 | 41 | |
| 10 | 2017 | 40 | |
| 11 | 2017 | 31 | |
| 12 | 2017 | 30 | |
| 13 | 2017 | 29 | |
| 14 | 2016 | 26 | |
| 15 | 2007 | 25 | |
| 16 | 2006 | 18 | |
| 17 | 2016 | 16 | |
| 18 | 2008 | 15 | |
| 19 | 2020 | 12 | |
| 20 | 2019 | 12 |
About Lejun Yu
Lejun Yu is a scholar working on Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Signal Processing, Artificial Intelligence and Biomaterials, having authored 59 papers that have together received 1.0k indexed citations. Recurring topics across this work include Emotion and Mood Recognition (21 papers), Human Pose and Action Recognition (11 papers), Face recognition and analysis (10 papers), Face and Expression Recognition (8 papers), Advanced Image Processing Techniques (6 papers), Multimodal Machine Learning Applications (6 papers), Video Surveillance and Tracking Methods (6 papers) and Advanced Vision and Imaging (5 papers). The work is most often cited by research in Experimental and Cognitive Psychology (334 citations), Computer Vision and Pattern Recognition (332 citations), Biomaterials (175 citations), Aquatic Science (83 citations) and Human-Computer Interaction (69 citations). Lejun Yu has collaborated with scholars based in China and Austria. Frequent co-authors include Jun He, Bo Sun, Chen‐Guang Liu, Zhe Chi, Fei Jiang, Mazhar Ali Raja, Chenguang Liu, Ziyu Shao, Xiaochen Cheng and Chengbo Li. Their work appears in journals such as International Journal of Biological Macromolecules, The Visual Computer, IEEE Transactions on Consumer Electronics, Neural Computing and Applications and IEEE Transactions on Affective Computing.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.