Umur Aybars Çiftçi
- Computer Vision and Pattern Recognition top 1%
- Experimental and Cognitive Psychology top 2%
- Artificial Intelligence top 10%
- Cognitive Neuroscience top 10%
- Biomedical Engineering
- Topics
- Generative Adversarial Networks and Image Synthesis (4 papers)Emotion and Mood Recognition (3 papers)Face recognition and analysis (3 papers)
- Cited by
- Computer Vision and Pattern RecognitionExperimental and Cognitive PsychologyHuman-Computer Interaction
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Affective ComputingThe Visual Computer
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Umur Aybars Çiftçi
11 papers receiving 979 citations
Hit Papers
Peers
Comparison fields: 5 of 64
- Computer Vision and Pattern Recognition 725
- Experimental and Cognitive Psychology 410
- Artificial Intelligence 195
- Cognitive Neuroscience 125
- Biomedical Engineering 121
Countries citing papers authored by Umur Aybars Çiftçi
This map shows the geographic impact of Umur Aybars Çiftçi'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 Umur Aybars Çiftçi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Umur Aybars Çiftçi more than expected).
Fields of papers citing papers by Umur Aybars Çiftçi
This network shows the impact of papers produced by Umur Aybars Çiftçi. 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 Umur Aybars Çiftçi. The network helps show where Umur Aybars Çiftçi may publish in the future.
Co-authorship network of co-authors of Umur Aybars Çiftçi
This figure shows the co-authorship network connecting the top 25 collaborators of Umur Aybars Çiftçi. A scholar is included among the top collaborators of Umur Aybars Çiftçi 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 Umur Aybars Çiftçi. Umur Aybars Çiftçi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 6 | |
| 4 | 1 | |
| 5 | 5 | |
| 6 | 14 | |
| 7 | FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signalsbreakdown → | 317 |
| 8 | 72 | |
| 9 | 4 | |
| 10 | Facial Expression Recognition by De-expression Residue Learningbreakdown → | 308 |
| 11 | 286 | |
| 12 | 3 |
About Umur Aybars Çiftçi
Umur Aybars Çiftçi is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Instrumentation, having authored 12 papers that have together received 1.0k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (4 papers), Emotion and Mood Recognition (3 papers) and Face recognition and analysis (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (725 citations), Experimental and Cognitive Psychology (410 citations) and Human-Computer Interaction (52 citations). Umur Aybars Çiftçi has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Lijun Yin, İlke Demir, Huiyuan Yang, Qiang Ji, Jeffrey F. Cohn, Zheng Zhang, Jeffrey M. Girard, Yue Wu, Peng Liu and Michael Reale. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Affective Computing and The Visual Computer.
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.