Koichiro Niinuma
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 5%
- Information Systems top 5%
- Computer Graphics and Computer-Aided Design top 5%
- Artificial Intelligence
- Co-authors
- Anil K. JainUnsang ParkAtsuto MakiHu HanTakashi MatsuyamaLászló A. JeniJeffrey F. CohnT. Takai
- Topics
- Face recognition and analysis (7 papers)Emotion and Mood Recognition (4 papers)Face and Expression Recognition (4 papers)
- Cited by
- Signal ProcessingComputer Graphics and Computer-Aided DesignComputer Vision and Pattern Recognition
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Information Forensics and SecurityComputer Vision and Image Understanding
- Partner nations
- United StatesJapanNetherlands
In The Last Decade
Koichiro Niinuma
16 papers receiving 288 citations
Peers
Comparison fields: 5 of 51
- Computer Vision and Pattern Recognition 181
- Signal Processing 127
- Information Systems 123
- Computer Graphics and Computer-Aided Design 36
- Artificial Intelligence 32
Countries citing papers authored by Koichiro Niinuma
This map shows the geographic impact of Koichiro Niinuma'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 Koichiro Niinuma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Koichiro Niinuma more than expected).
Fields of papers citing papers by Koichiro Niinuma
This network shows the impact of papers produced by Koichiro Niinuma. 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 Koichiro Niinuma. The network helps show where Koichiro Niinuma may publish in the future.
Co-authorship network of co-authors of Koichiro Niinuma
This figure shows the co-authorship network connecting the top 25 collaborators of Koichiro Niinuma. A scholar is included among the top collaborators of Koichiro Niinuma 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 Koichiro Niinuma. Koichiro Niinuma 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 | 0 | |
| 3 | 1 | |
| 4 | 6 | |
| 5 | 0 | |
| 6 | 3 | |
| 7 | 10 | |
| 8 | 13 | |
| 9 | 1 | |
| 10 | 2 | |
| 11 | 3 | |
| 12 | 2 | |
| 13 | 3 | |
| 14 | Unmasking the Devil in the Details: What Works for Deep Facial Action Coding? | 9 |
| 15 | 28 | |
| 16 | 138 | |
| 17 | 33 | |
| 18 | 34 | |
| 19 | 17 |
About Koichiro Niinuma
Koichiro Niinuma is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Experimental and Cognitive Psychology, having authored 19 papers that have together received 303 indexed citations. Recurring topics across this work include Face recognition and analysis (7 papers), Emotion and Mood Recognition (4 papers) and Face and Expression Recognition (4 papers). The work is most often cited by research in Signal Processing (127 citations), Computer Graphics and Computer-Aided Design (36 citations) and Computer Vision and Pattern Recognition (181 citations). Koichiro Niinuma has collaborated with scholars based in United States, Japan and Netherlands. Frequent co-authors include Anil K. Jain, Unsang Park, Atsuto Maki, Hu Han, Takashi Matsuyama, László A. Jeni, Jeffrey F. Cohn, T. Takai, Heng Yu and Itır Önal Ertuğrul. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Information Forensics and Security and Computer Vision and Image Understanding.
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.