Shan Li

5.3k total citations · 3 hit papers
69 papers, 3.4k citations indexed

About

Shan Li is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology and Experimental and Cognitive Psychology. According to data from OpenAlex, Shan Li has authored 69 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 15 papers in Molecular Biology and 10 papers in Experimental and Cognitive Psychology. Recurrent topics in Shan Li's work include Face and Expression Recognition (10 papers), Emotion and Mood Recognition (8 papers) and Face recognition and analysis (5 papers). Shan Li is often cited by papers focused on Face and Expression Recognition (10 papers), Emotion and Mood Recognition (8 papers) and Face recognition and analysis (5 papers). Shan Li collaborates with scholars based in China, United States and Australia. Shan Li's co-authors include Weihong Deng, Junping Du, Zhenqi Xu, Ailin Luo, Chun Yang, Bin Zhu, Gaofeng Zhan, Niannian Huang, Ling Yang and Ning Yang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Clinical Endocrinology & Metabolism and The Plant Journal.

In The Last Decade

Shan Li

65 papers receiving 3.3k citations

Hit Papers

Deep Facial Expression Recognition: A Survey 2017 2026 2020 2023 2020 2017 2018 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep Facial Expression Recognition: A Survey
    2020 913 citations Shan Li, Weihong Deng profile →
  2. Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild
    2017 786 citations Shan Li, Weihong Deng et al. profile →
  3. Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition
    2018 447 citations Shan Li, Weihong Deng IEEE Transactions on Image Processing profile →

Peers

Shan Li
Wen‐Jing Yan China
Shangfei Wang China
Michael J. Lyons Japan
Gwen Littlewort United States
Ana Fred Portugal
Guangyuan Liu China
Ira L. Cohen United States
Zheru Chi Hong Kong
Irene Kotsia United Kingdom
Ian Fasel United States
Wen‐Jing Yan China
Shan Li
Citations per year, relative to Shan Li Shan Li (= 1×) peers Wen‐Jing Yan

Countries citing papers authored by Shan Li

Since Specialization
Citations

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

Fields of papers citing papers by Shan Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shan Li

This figure shows the co-authorship network connecting the top 25 collaborators of Shan Li. A scholar is included among the top collaborators of Shan Li 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 Shan Li. Shan Li 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.
2.
Wu, Lijun, et al.. (2025). Dilated-convolutional feature modulation network for efficient image super-resolution. Journal of Real-Time Image Processing. 22(2). 1 indexed citations
3.
Li, Yuanyuan, Mingming Yuan, Shan Li, et al.. (2024). Cell-free DNA methylation reveals cell-specific tissue injury and correlates with disease severity and patient outcomes in COVID-19. Clinical Epigenetics. 16(1). 37–37. 2 indexed citations
4.
Young, J. Nilas, et al.. (2024). AV1/AVM development at Google. 34–34.
5.
Zhang, Chi, Linqi Ouyang, Wen Wen, et al.. (2023). Anaphylactoid reactions induced by Shuanghuanglian injection and Shenmai injection and metabolomics analysis. Frontiers in Pharmacology. 14. 1200199–1200199. 2 indexed citations
6.
7.
Qi, Yu, et al.. (2023). Pathogenic bi‐allelic variants of meiotic ZMM complex gene SPO16 in premature ovarian insufficiency. Clinical Genetics. 104(4). 486–490. 4 indexed citations
8.
Li, Shan, et al.. (2023). The Adjustment of Complexity on Sarcasm Processing in Chinese: Evidence from Reading Time Indicators. Brain Sciences. 13(2). 207–207. 1 indexed citations
9.
Wang, Xinfeng, et al.. (2022). Analysis of Surface Deformation and Settlement Characteristics Caused by Tunnel Excavation and Unloading. Geofluids. 2022. 1–15. 3 indexed citations
10.
Zhang, Qian, Shan Li, Bingying Xu, et al.. (2022). Homozygous Variant in KASH5 Causes Premature Ovarian Insufficiency by Disordered Meiotic Homologous Pairing. The Journal of Clinical Endocrinology & Metabolism. 107(9). 2589–2597. 10 indexed citations
11.
Guo, Ting, Hanni Ke, Qian Zhang, et al.. (2021). Pathogenic variants of meiotic double strand break (DSB) formation genes PRDM9 and ANKRD31 in premature ovarian insufficiency. Genetics in Medicine. 23(12). 2309–2315. 15 indexed citations
12.
Li, Shan, Guanze Liu, Xuyan Liu, et al.. (2021). WRKY Transcription Factors Actively Respond to Fusarium oxysporum in Lilium regale. Phytopathology. 111(9). 1625–1637. 18 indexed citations
13.
Li, Shan, Xiaoxiao Luo, Dongyu Hua, et al.. (2020). Ketamine Alleviates Postoperative Depression-Like Symptoms in Susceptible Mice: The Role of BDNF-TrkB Signaling. Frontiers in Pharmacology. 10. 1702–1702. 34 indexed citations
14.
Olson, Nathan D., M. S. Binoj Kumar, Shan Li, et al.. (2020). A framework for assessing 16S rRNA marker-gene survey data analysis methods using mixtures.. Microbiome. 8(1). 35–35. 8 indexed citations
15.
Li, Shan & Ivan Ovcharenko. (2019). Enhancer jungles establish robust tissue-specific regulatory control in the human genome. Genomics. 112(3). 2261–2270. 3 indexed citations
16.
Li, Shan & Weihong Deng. (2018). Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition. IEEE Transactions on Image Processing. 28(1). 356–370. 447 indexed citations breakdown →
17.
Wen, Zhigang, et al.. (2018). Robust Downlink Beamforming for Multiuser SWIPT Systems with Imperfect CSI. Journal of Signal Processing Systems. 90(6). 849–855. 2 indexed citations
18.
Wang, Yuzhou, et al.. (2017). Effects of capsaicin on the cholesterol lithogenesis in the gallbladder of C57BL/6 mice. Data Archiving and Networked Services (DANS). 10(2). 2066–2075. 2 indexed citations
19.
Li, Shan & Weihong Deng. (2016). Real world expression recognition: A highly imbalanced detection problem. 1–6. 8 indexed citations
20.
Xu, Ying, Dan Lin, Shan Li, et al.. (2009). Curcumin reverses impaired cognition and neuronal plasticity induced by chronic stress. Neuropharmacology. 57(4). 463–471. 96 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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