Dae Ha Kim

52 total papers · 529 total citations
30 papers, 226 citations indexed

About

Dae Ha Kim is a scholar working on Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology and Artificial Intelligence. According to data from OpenAlex, Dae Ha Kim has authored 30 papers receiving a total of 226 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 10 papers in Experimental and Cognitive Psychology and 9 papers in Artificial Intelligence. Recurrent topics in Dae Ha Kim's work include Emotion and Mood Recognition (10 papers), Face and Expression Recognition (9 papers) and Face recognition and analysis (6 papers). Dae Ha Kim is often cited by papers focused on Emotion and Mood Recognition (10 papers), Face and Expression Recognition (9 papers) and Face recognition and analysis (6 papers). Dae Ha Kim collaborates with scholars based in South Korea, United States and Germany. Dae Ha Kim's co-authors include Byung Cheol Song, Seunghyun Lee, JungHo Jeon, Seung Hyun Lee, Seunghyun Kang, Joon‐Hyuk Chang, Taeyoung Han, Jaewoong Choi, Deok‐Hwan Kim and Sang-Hyuk Lee and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Sensors.

In The Last Decade

Dae Ha Kim

28 papers receiving 219 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Dae Ha Kim 125 86 66 38 17 30 226
Fei Long 154 1.2× 54 0.6× 71 1.1× 18 0.5× 21 1.2× 36 238
Séverine Dubuisson 111 0.9× 81 0.9× 56 0.8× 43 1.1× 7 0.4× 23 253
Amanjot Kaur 122 1.0× 112 1.3× 87 1.3× 18 0.5× 10 0.6× 40 304
Alia Karim Abdul Hassan 98 0.8× 58 0.7× 115 1.7× 37 1.0× 26 1.5× 53 266
Mingqiang Yang 89 0.7× 35 0.4× 42 0.6× 19 0.5× 12 0.7× 17 197
Anima Majumder 228 1.8× 166 1.9× 44 0.7× 26 0.7× 13 0.8× 20 319
Jinhee Chun 156 1.2× 95 1.1× 47 0.7× 34 0.9× 8 0.5× 19 233
Chiara Pero 199 1.6× 73 0.8× 51 0.8× 81 2.1× 6 0.4× 33 315
Swati Nigam 207 1.7× 61 0.7× 76 1.2× 9 0.2× 16 0.9× 38 296
Gengming Zhu 162 1.3× 32 0.4× 60 0.9× 49 1.3× 10 0.6× 19 275

Countries citing papers authored by Dae Ha Kim

Since Specialization
Citations

This map shows the geographic impact of Dae Ha Kim'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 Dae Ha Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dae Ha Kim more than expected).

Fields of papers citing papers by Dae Ha Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dae Ha Kim. 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 Dae Ha Kim. The network helps show where Dae Ha Kim may publish in the future.

Co-authorship network of co-authors of Dae Ha Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Dae Ha Kim. A scholar is included among the top collaborators of Dae Ha Kim 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 Dae Ha Kim. Dae Ha Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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.

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2026