Choon-Ching Ng
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Endocrinology, Diabetes and Metabolism
- Experimental and Cognitive Psychology
- Dermatology
- Co-authors
- Moi Hoon YapBaihua LiNicholas CostenAdrian K. DavisonCliff LansleyGee-Sern HsuNeil D. ReevesFrank L. Bowling
- Topics
- Face recognition and analysis (7 papers)Generative Adversarial Networks and Image Synthesis (4 papers)Facial Rejuvenation and Surgery Techniques (3 papers)
- Partner nations
- United KingdomMalaysiaTaiwan
In The Last Decade
Choon-Ching Ng
11 papers receiving 231 citations
Peers
Comparison fields: 5 of 60
- Computer Vision and Pattern Recognition 141
- Artificial Intelligence 48
- Endocrinology, Diabetes and Metabolism 39
- Experimental and Cognitive Psychology 32
- Dermatology 30
Countries citing papers authored by Choon-Ching Ng
This map shows the geographic impact of Choon-Ching Ng'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 Choon-Ching Ng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Choon-Ching Ng more than expected).
Fields of papers citing papers by Choon-Ching Ng
This network shows the impact of papers produced by Choon-Ching Ng. 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 Choon-Ching Ng. The network helps show where Choon-Ching Ng may publish in the future.
Co-authorship network of co-authors of Choon-Ching Ng
This figure shows the co-authorship network connecting the top 25 collaborators of Choon-Ching Ng. A scholar is included among the top collaborators of Choon-Ching Ng 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 Choon-Ching Ng. Choon-Ching Ng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 16 | |
| 2 | 30 | |
| 3 | 46 | |
| 4 | 7 | |
| 5 | 55 | |
| 6 | 23 | |
| 7 | 10 | |
| 8 | 35 | |
| 9 | An investigation on local wrinkle-based extractor of age estimation | 10 |
| 10 | 0 | |
| 11 | 4 | |
| 12 | Arabic Script Web Document Language Identifications Using Neural Network | 1 |
About Choon-Ching Ng
Choon-Ching Ng is a scholar working on Computer Vision and Pattern Recognition, Dermatology and Occupational Therapy, having authored 12 papers that have together received 237 indexed citations. Recurring topics across this work include Face recognition and analysis (7 papers), Generative Adversarial Networks and Image Synthesis (4 papers) and Facial Rejuvenation and Surgery Techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (141 citations), Health Information Management (22 citations) and Occupational Therapy (15 citations). Choon-Ching Ng has collaborated with scholars based in United Kingdom, Malaysia and Taiwan. Frequent co-authors include Moi Hoon Yap, Baihua Li, Nicholas Costen, Adrian K. Davison, Cliff Lansley, Gee-Sern Hsu, Neil D. Reeves, Frank L. Bowling, Katie E. Chatwin and Andrew J.M. Boulton. Their work appears in journals such as IEEE Access, Image and Vision Computing and Journal of Diabetes Science and Technology.
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.