Deepali Aneja
Impact in
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- Face recognition and analysis
- Face and Expression Recognition
- Generative Adversarial Networks and Image Synthesis
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- Emotion and Mood Recognition
Papers in
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- Human Pose and Action Recognition 4
- Video Analysis and Summarization 3
- Advanced Vision and Imaging 2
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- Human Motion and Animation 4
- Co-authors
- Linda G. Shapiro (4 shared papers)Alex Colburn (2 shared papers)Tarun Kumar Rawat (1 shared paper)Daniel McDuff (2 shared papers)Mary Czerwinski (2 shared papers)Frederick Shic (1 shared paper)Beibin Li (1 shared paper)Pamela Ventola (1 shared paper)
- Journals
- ACM Transactions on Graphics (1 paper)Lecture notes in computer science (1 paper)International Journal of Intelligent Systems and Applications (1 paper)PubMed (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2 papers)
- Partner nations
- United StatesChinaJapan
In The Last Decade
Deepali Aneja
12 papers receiving 262 citations
Peers
Comparison fields: 5 of 56
- Computer Vision and Pattern Recognition 153
- Experimental and Cognitive Psychology 75
- Human-Computer Interaction 34
- Cognitive Neuroscience 41
- Artificial Intelligence 67
Countries citing papers authored by Deepali Aneja
This map shows the geographic impact of Deepali Aneja'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 Deepali Aneja with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepali Aneja more than expected).
Fields of papers citing papers by Deepali Aneja
This network shows the impact of papers produced by Deepali Aneja. 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 Deepali Aneja. The network helps show where Deepali Aneja may publish in the future.
Co-authors
The 25 scholars most cited alongside Deepali Aneja, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 99 | |
| 2 | 2019 | 41 | |
| 3 | 2013 | 29 | |
| 4 | 2021 | 29 | |
| 5 | 2018 | 18 | |
| 6 | 2015 | 15 | |
| 7 | 2022 | 15 | |
| 8 | 2020 | 11 | |
| 9 | 2024 | 6 | |
| 10 | 2022 | 4 | |
| 11 | 2024 | 3 | |
| 12 | 2023 | 2 |
About Deepali Aneja
Deepali Aneja is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering, Social Psychology, Artificial Intelligence and Geometry and Topology, having authored 12 papers that have together received 272 indexed citations. Recurring topics across this work include Human Motion and Animation (4 papers), Human Pose and Action Recognition (4 papers), Video Analysis and Summarization (3 papers), Advanced Vision and Imaging (2 papers), Social Robot Interaction and HRI (2 papers), AI in Service Interactions (2 papers), Forensic Anthropology and Bioarchaeology Studies (1 paper) and Hand Gesture Recognition Systems (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (153 citations), Experimental and Cognitive Psychology (75 citations), Human-Computer Interaction (34 citations), Cognitive Neuroscience (41 citations) and Artificial Intelligence (67 citations). Deepali Aneja has collaborated with scholars based in United States, China and Japan. Frequent co-authors include Linda G. Shapiro, Alex Colburn, Tarun Kumar Rawat, Daniel McDuff, Mary Czerwinski, Frederick Shic, Beibin Li, Pamela Ventola, Sachin Mehta and Evangelos Kalogerakis. Their work appears in journals such as ACM Transactions on Graphics, Lecture notes in computer science, International Journal of Intelligent Systems and Applications, PubMed and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.