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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
DLME: Deep Local-Flatness Manifold Embedding
TL;DR: Deep Local-flatness Manifold Embedding (DLME) as mentioned in this paper constructs semantic manifolds by data augmentation and overcomes the structural distortion problem using a smoothness constrained based on a local flatness assumption about the manifold.
Book ChapterDOI
Single Shot Attention-Based Face Detector
TL;DR: An one-stage face detector, named Single Shot Attention-Based Face Detector (AFD), which enables accurate detection of multi-scale faces with high efficiency, especially for small faces.
Proceedings ArticleDOI
Reducing Impact of Inaccurate User Feedback in Face Retrieval
TL;DR: A new query point movement technique for target search is proposed by posing the problem of reducing the impact of inaccurate user feedback as an optimization problem and a rank function for finding target images is proposed, which would assign high scores to the images near the relevant images and punish those close to the decision boundary.
Posted Content
Static and Dynamic Fusion for Multi-modal Cross-ethnicity Face Anti-spoofing
TL;DR: This work proposes a static-dynamic fusion mechanism for multi-modal face anti-spoofing, inspired by motion divergences between real and fake faces, and develops a partially shared fusion method to learn complementary information from multiple modalities.
Proceedings ArticleDOI
Evaluation of face alignment solutions using statistical learning
TL;DR: Experimental results demonstrate that the classification function learned using the proposed approach provides semantically more meaningful scoring than the reconstruction error used in AAM for classification between qualified and un-qualified face alignment.