Meiling Fang
Impact in
- Signal Processing top 5%
- Biometric Identification and Security
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- Face recognition and analysis
- Face and Expression Recognition
- Digital Media Forensic Detection
- Video Surveillance and Tracking Methods
Papers in
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- Face recognition and analysis 20
- Face and Expression Recognition 6
- Generative Adversarial Networks and Image Synthesis 2
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- Biometric Identification and Security 18
- Co-authors
- Naser Damer (23 shared papers)Arjan Kuijper (16 shared papers)Florian Kirchbuchner (11 shared papers)Fadi Boutros (14 shared papers)Marco Huber (3 shared papers)Vitomir Štruc (1 shared paper)Aifeng Ren (1 shared paper)Kamyar Mehran (1 shared paper)
In The Last Decade
Meiling Fang
26 papers receiving 365 citations
Peers
Comparison fields: 5 of 47
- Signal Processing 236
- Computer Vision and Pattern Recognition 291
- Information Systems 62
- Safety Research 22
- Artificial Intelligence 59
Countries citing papers authored by Meiling Fang
This map shows the geographic impact of Meiling Fang'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 Meiling Fang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Meiling Fang more than expected).
Fields of papers citing papers by Meiling Fang
This network shows the impact of papers produced by Meiling Fang. 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 Meiling Fang. The network helps show where Meiling Fang may publish in the future.
Co-authors
The 25 scholars most cited alongside Meiling Fang, 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 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 44 | |
| 2 | 2021 | 43 | |
| 3 | 2022 | 27 | |
| 4 | 2023 | 26 | |
| 5 | 2021 | 26 | |
| 6 | 2023 | 20 | |
| 7 | 2023 | 19 | |
| 8 | 2020 | 18 | |
| 9 | 2020 | 18 | |
| 10 | 2020 | 18 | |
| 11 | 2022 | 16 | |
| 12 | 2021 | 15 | |
| 13 | 2023 | 15 | |
| 14 | 2022 | 12 | |
| 15 | 2021 | 10 | |
| 16 | 2023 | 10 | |
| 17 | 2017 | 7 | |
| 18 | Merge-SfM: Merging Partial Reconstructions. | 2019 | 6 |
| 19 | 2022 | 5 | |
| 20 | 2024 | 5 |
About Meiling Fang
Meiling Fang is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Information Systems, Artificial Intelligence and Genetics, having authored 27 papers that have together received 378 indexed citations. Recurring topics across this work include Face recognition and analysis (20 papers), Biometric Identification and Security (18 papers), Face and Expression Recognition (6 papers), Forensic and Genetic Research (3 papers), Digital and Cyber Forensics (3 papers), User Authentication and Security Systems (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Signal Processing (236 citations), Computer Vision and Pattern Recognition (291 citations), Information Systems (62 citations), Safety Research (22 citations) and Artificial Intelligence (59 citations). Meiling Fang has collaborated with scholars based in Germany, China and India. Frequent co-authors include Naser Damer, Arjan Kuijper, Florian Kirchbuchner, Fadi Boutros, Marco Huber, Vitomir Štruc, Aifeng Ren, Kamyar Mehran, Zhiya Zhang and Yang Hao. Their work appears in journals such as Pattern Recognition, Image and Vision Computing, IET Microwaves Antennas & Propagation, Machine Vision and Applications and International Journal of Molecular Sciences.
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.