Shiguang Shan

34.7k total citations · 11 hit papers
412 papers, 19.4k citations indexed

About

Shiguang Shan is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Shiguang Shan has authored 412 papers receiving a total of 19.4k indexed citations (citations by other indexed papers that have themselves been cited), including 346 papers in Computer Vision and Pattern Recognition, 84 papers in Signal Processing and 80 papers in Artificial Intelligence. Recurrent topics in Shiguang Shan's work include Face recognition and analysis (196 papers), Face and Expression Recognition (180 papers) and Video Surveillance and Tracking Methods (72 papers). Shiguang Shan is often cited by papers focused on Face recognition and analysis (196 papers), Face and Expression Recognition (180 papers) and Video Surveillance and Tracking Methods (72 papers). Shiguang Shan collaborates with scholars based in China, United States and Singapore. Shiguang Shan's co-authors include Xilin Chen, Ruiping Wang, Meina Kan, Wen Gao, Wen Gao, Hu Han, Hong Chang, Jiabei Zeng, Bo Cao and Xilin Chen and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Expert Systems with Applications.

In The Last Decade

Shiguang Shan

390 papers receiving 18.7k citations

Hit Papers

The CAS-PEAL Large-Scale Chinese Face Database and Baseli... 2005 2026 2012 2019 2007 2009 2005 2018 2016 250 500 750

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Shiguang Shan China 71 16.0k 3.8k 3.7k 2.0k 1.6k 412 19.4k
Xilin Chen China 68 15.0k 0.9× 3.3k 0.9× 3.4k 0.9× 1.8k 0.9× 1.5k 1.0× 475 19.5k
Stan Z. Li China 74 16.6k 1.0× 5.5k 1.4× 3.4k 0.9× 875 0.4× 1.5k 1.0× 338 21.2k
Ioannis Pitas Greece 62 12.4k 0.8× 3.0k 0.8× 2.9k 0.8× 1.3k 0.6× 1.5k 1.0× 656 16.3k
Wen Gao China 66 16.7k 1.0× 4.8k 1.3× 2.8k 0.8× 819 0.4× 2.6k 1.6× 890 21.3k
Nicu Sebe Italy 72 13.7k 0.9× 1.4k 0.4× 5.5k 1.5× 3.0k 1.5× 1.5k 1.0× 510 20.1k
P. Jonathon Phillips United States 42 14.3k 0.9× 5.4k 1.4× 1.6k 0.4× 725 0.4× 1.3k 0.8× 116 16.6k
Bill Triggs France 28 22.7k 1.4× 1.6k 0.4× 4.7k 1.3× 654 0.3× 3.3k 2.1× 70 27.0k
Tieniu Tan China 74 16.3k 1.0× 5.8k 1.5× 4.4k 1.2× 336 0.2× 2.0k 1.3× 389 22.4k
Matthew Turk United States 31 12.9k 0.8× 3.6k 0.9× 1.8k 0.5× 488 0.2× 1.8k 1.1× 77 15.5k
Honglak Lee United States 48 8.0k 0.5× 1.9k 0.5× 6.5k 1.7× 464 0.2× 872 0.6× 135 14.8k

Countries citing papers authored by Shiguang Shan

Since Specialization
Citations

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

Fields of papers citing papers by Shiguang Shan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shiguang Shan. 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 Shiguang Shan. The network helps show where Shiguang Shan may publish in the future.

Co-authorship network of co-authors of Shiguang Shan

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

All Works

20 of 20 papers shown
1.
Li, Yong, et al.. (2025). Instance-Consistent Fair Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(7). 5319–5335. 2 indexed citations
2.
Yuan, Yu-Jie, et al.. (2025). StylizedGS: Controllable Stylization for 3D Gaussian Splatting. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(12). 11961–11973. 1 indexed citations
3.
Kan, Meina, Lixuan Zhang, Liang Fan, et al.. (2025). eLabrador: A Wearable Navigation System for Visually Impaired Individuals. IEEE Transactions on Automation Science and Engineering. 22. 12228–12244. 1 indexed citations
4.
Zeng, Jiabei, et al.. (2025). Exp-VQA: Fine-grained facial expression analysis via visual question answering. Pattern Recognition. 168. 111783–111783.
6.
Shan, Shiguang, et al.. (2025). PIT: A Plug-and-Play Image Translator for Making Off-the-Shelf Models Adapt to Corruptions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(12). 11644–11661.
7.
Han, Hu, et al.. (2024). Fine-Grained Open-Set Deepfake Detection via Unsupervised Domain Adaptation. IEEE Transactions on Information Forensics and Security. 19. 7536–7547. 3 indexed citations
8.
Li, Qiankun, et al.. (2023). Data-Efficient Masked Video Modeling for Self-supervised Action Recognition. 2723–2733. 7 indexed citations
9.
Gao, Difei, Ruiping Wang, Shiguang Shan, & Xilin Chen. (2022). CRIC: A VQA Dataset for Compositional Reasoning on Vision and Commonsense. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(5). 5561–5578. 13 indexed citations
10.
Li, Yong & Shiguang Shan. (2021). Meta Auxiliary Learning for Facial Action Unit Detection. IEEE Transactions on Affective Computing. 14(3). 2526–2538. 13 indexed citations
11.
Zhang, Jie, et al.. (2020). Single-Side Domain Generalization for Face Anti-Spoofing. 8481–8490. 192 indexed citations
12.
Li, Yong, Jiabei Zeng, & Shiguang Shan. (2020). Learning Representations for Facial Actions From Unlabeled Videos. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(1). 302–317. 28 indexed citations
13.
Kan, Meina, et al.. (2020). Learning deep face representation with long-tail data: An aggregate-and-disperse approach. Pattern Recognition Letters. 133. 48–54. 6 indexed citations
14.
Jing, Xiao‐Yuan, Xinyu Zhang, Xiaoke Zhu, et al.. (2019). Multiset Feature Learning for Highly Imbalanced Data Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(1). 139–156. 106 indexed citations
15.
Zhang, Fangyi, Bingpeng Ma, Hong Chang, Shiguang Shan, & Xilin Chen. (2019). Relation-aware Multiple Attention Siamese Networks for Robust Visual Tracking.. British Machine Vision Conference. 290. 1 indexed citations
16.
Zhang, Hongkai, Hong Chang, Bingpeng Ma, Shiguang Shan, & Xilin Chen. (2019). Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection.. arXiv (Cornell University). 227. 6 indexed citations
17.
Li, Xiaoyan, Meina Kan, Shiguang Shan, & Xilin Chen. (2019). Weakly Supervised Object Detection With Segmentation Collaboration. 9734–9743. 74 indexed citations
18.
Liu, Yong, Ruiping Wang, Shiguang Shan, & Xilin Chen. (2018). Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships. 6985–6994. 170 indexed citations
19.
Li, Yong, Jiabei Zeng, Shiguang Shan, & Xilin Chen. (2018). Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism. IEEE Transactions on Image Processing. 28(5). 2439–2450. 632 indexed citations breakdown →
20.
Li, Shaoxin, Shiguang Shan, Shuicheng Yan, & Xilin Chen. (2016). Relative Forest for Visual Attribute Prediction. IEEE Transactions on Image Processing. 25(9). 3991–4003. 5 indexed citations

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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