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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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Proceedings ArticleDOI
Moving Cast Shadow Removal Based on Local Descriptors
TL;DR: A novel algorithm for detection of moving cast shadows, that based on a local texture descriptor called Scale Invariant Local Ternary Pattern (SILTP) is presented, which demonstrates the robustness of the algorithm.
Journal ArticleDOI
Faceboxes: A CPU real-time and accurate unconstrained face detector
TL;DR: A novel face detector, named FaceBoxes, with superior performance on both speed and accuracy is proposed, and a new anchor densification strategy to make different types of anchors have the same density on the image, which significantly improves the recall rate of small faces.
Journal ArticleDOI
Single-Shot Scale-Aware Network for Real-Time Face Detection
TL;DR: This work proposes a scale-aware detection network using a wide scale range of layers associated with appropriate scales of anchors to handle faces with various scales, and describes a new equal density principle to ensure anchors with different scales to be evenly distributed on the image.
Journal ArticleDOI
RefineFace: Refinement Neural Network for High Performance Face Detection
TL;DR: RefineFace as mentioned in this paper is a single-shot refinement face detector consisting of five modules: selective two-step regression, selective two step classification, scale-aware margin loss, feature supervision module, and receptive field enhancement.
Posted Content
CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing
TL;DR: This work introduces the largest CASIA-SURF Cross-ethnicity Face Anti-spoofing (CeFA) dataset, and proposes a novel multi-modal fusion method as a strong baseline to alleviate the ethnic bias.