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Stan Z. Li
Researcher at Westlake University
Publications - 625
Citations - 49737
Stan Z. Li is an academic researcher from Westlake University. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 97, co-authored 532 publications receiving 41793 citations. Previous affiliations of Stan Z. Li include Microsoft & Macau University of Science and Technology.
Papers
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Book ChapterDOI
Structured ordinal features for appearance-based object representation
TL;DR: This paper extends SOF to Multi-scale Structured Ordinal Feature (MSOF) by concatenating binary strings of multi-scale SOFs at a fix position, which encodes not only microstructure but also macrostructure of image patterns, thus provides a more powerful image representation.
Posted Content
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing
Shifeng Zhang,Xiaobo Wang,Ajian Liu,Chenxu Zhao,Jun Wan,Sergio Escalera,Hailin Shi,Zezheng Wang,Stan Z. Li +8 more
TL;DR: CASIA-SURF as mentioned in this paper is a large-scale multi-modal dataset for face anti-spoofing, which consists of $1,000$ subjects with $21, 000$ videos and each sample has $3$ modalities.
Journal Article
SemiRetro: Semi-template framework boosts deep retrosynthesis prediction
TL;DR: The method SemiRetro is called, a new GNN layer (DRGAT) is introduced to enhance center identification, and a novel self-correcting module to improve semi-template classification is proposed to combine both advantages of TB and TF.
Proceedings ArticleDOI
View-subspace analysis of multi-view face patterns
TL;DR: In this paper, view-specific basis components can be learned from multi-view face examples in an unsupervised way by using ICA, ISA and TICA; whereas the components learned by using principal component analysis reveal little view-related information.
Book ChapterDOI
Statistical learning of evaluation function for ASM/AAM image alignment
TL;DR: An effective method for the evaluation of ASM/AAM alignment results has been lacking and a bad alignment cannot be identified and this can drop system performance.