Wen-Sheng Chu
- Computer Vision and Pattern Recognition top 1%
- Experimental and Cognitive Psychology top 1%
- Signal Processing top 2%
- Artificial Intelligence top 5%
- Cognitive Neuroscience top 10%
- Co-authors
- Fernando De la TorreJeffrey F. CohnKaili ZhaoHonggang ZhangJeffery F. CohnAlejandro JaimesYale SongXiaoyu Ding
- Topics
- Emotion and Mood Recognition (17 papers)Face and Expression Recognition (12 papers)Face recognition and analysis (9 papers)
- Cited by
- Experimental and Cognitive PsychologyComputer Vision and Pattern RecognitionSignal Processing
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Image ProcessingPattern Recognition
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Wen-Sheng Chu
25 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 100
- Computer Vision and Pattern Recognition 1.2k
- Experimental and Cognitive Psychology 835
- Signal Processing 272
- Artificial Intelligence 267
- Cognitive Neuroscience 186
Countries citing papers authored by Wen-Sheng Chu
This map shows the geographic impact of Wen-Sheng Chu'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 Wen-Sheng Chu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wen-Sheng Chu more than expected).
Fields of papers citing papers by Wen-Sheng Chu
This network shows the impact of papers produced by Wen-Sheng Chu. 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 Wen-Sheng Chu. The network helps show where Wen-Sheng Chu may publish in the future.
Co-authorship network of co-authors of Wen-Sheng Chu
This figure shows the co-authorship network connecting the top 25 collaborators of Wen-Sheng Chu. A scholar is included among the top collaborators of Wen-Sheng Chu 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 Wen-Sheng Chu. Wen-Sheng Chu 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 | 7 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | Chapter 19 - Affective facial computing: Generalizability across domains | 2 |
| 6 | 42 | |
| 7 | 15 | |
| 8 | 8 | |
| 9 | 49 | |
| 10 | 27 | |
| 11 | 60 | |
| 12 | 228 | |
| 13 | 9 | |
| 14 | 135 | |
| 15 | 13 | |
| 16 | 146 | |
| 17 | 164 | |
| 18 | 48 | |
| 19 | 58 | |
| 20 | 217 |
About Wen-Sheng Chu
Wen-Sheng Chu is a scholar working on Experimental and Cognitive Psychology, Computer Vision and Pattern Recognition and Signal Processing, having authored 26 papers that have together received 1.5k indexed citations. Recurring topics across this work include Emotion and Mood Recognition (17 papers), Face and Expression Recognition (12 papers) and Face recognition and analysis (9 papers). The work is most often cited by research in Experimental and Cognitive Psychology (835 citations), Computer Vision and Pattern Recognition (1.2k citations) and Signal Processing (272 citations). Wen-Sheng Chu has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Fernando De la Torre, Jeffrey F. Cohn, Kaili Zhao, Honggang Zhang, Jeffery F. Cohn, Fernando De la Torre, Alejandro Jaimes, Yale Song, Xiaoyu Ding and Xuehan Xiong. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Pattern Recognition.
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.