Masoud Faraki
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence
- Media Technology top 10%
- Biomedical Engineering
- Computational Mechanics
- Co-authors
- Mehrtash HarandiFatih PorikliArnold WiliemBrian C. LovellMaziar PalhangConrad SandersonManmohan ChandrakerSamuel Schulter
- Topics
- Face and Expression Recognition (7 papers)Domain Adaptation and Few-Shot Learning (6 papers)Video Surveillance and Tracking Methods (5 papers)
- Journals
- Pattern RecognitionIEEE Transactions on Neural Networks and Learning SystemsPattern Recognition Letters
- Partner nations
- AustraliaUnited StatesIran
In The Last Decade
Masoud Faraki
15 papers receiving 227 citations
Peers
Comparison fields: 5 of 53
- Computer Vision and Pattern Recognition 163
- Artificial Intelligence 70
- Media Technology 52
- Biomedical Engineering 19
- Computational Mechanics 17
Countries citing papers authored by Masoud Faraki
This map shows the geographic impact of Masoud Faraki'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 Masoud Faraki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Masoud Faraki more than expected).
Fields of papers citing papers by Masoud Faraki
This network shows the impact of papers produced by Masoud Faraki. 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 Masoud Faraki. The network helps show where Masoud Faraki may publish in the future.
Co-authorship network of co-authors of Masoud Faraki
This figure shows the co-authorship network connecting the top 25 collaborators of Masoud Faraki. A scholar is included among the top collaborators of Masoud Faraki based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Masoud Faraki. Masoud Faraki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 3 | |
| 3 | 10 | |
| 4 | 20 | |
| 5 | 19 | |
| 6 | 9 | |
| 7 | 3 | |
| 8 | 11 | |
| 9 | 14 | |
| 10 | 26 | |
| 11 | 19 | |
| 12 | 13 | |
| 13 | Bag of riemannian words for virus classification | 1 |
| 14 | 32 | |
| 15 | 49 |
About Masoud Faraki
Masoud Faraki is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence, having authored 15 papers that have together received 230 indexed citations. Recurring topics across this work include Face and Expression Recognition (7 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Video Surveillance and Tracking Methods (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (163 citations), Computational Mathematics (4 citations) and Media Technology (52 citations). Masoud Faraki has collaborated with scholars based in Australia, United States and Iran. Frequent co-authors include Mehrtash Harandi, Fatih Porikli, Arnold Wiliem, Brian C. Lovell, Maziar Palhang, Conrad Sanderson, Manmohan Chandraker, Samuel Schulter, Yumin Suh and Yi‐Hsuan Tsai. Their work appears in journals such as Pattern Recognition, IEEE Transactions on Neural Networks and Learning Systems and Pattern Recognition Letters.
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.