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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
Learning semantic scene models by object classification and trajectory clustering
TL;DR: In this framework, the detected moving objects are first classified as pedestrians or vehicles via a co-trained classifier which takes advantage of the multiview information of objects and can automatically learn motion patterns respectively for pedestrians and vehicles.
Journal ArticleDOI
Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection
TL;DR: This paper proposes a two-stream convolutional neural network (TSCNN), which works on two complementary spaces: RGB space ( original imaging space) and multi-scale retinex (MSR) space (illumination-invariant space), and proposes an attention-based fusion method, which can effectively capture the complementarity of two features.
Posted Content
FaceBoxes: A CPU Real-time Face Detector with High Accuracy
TL;DR: FaceBoxes as mentioned in this paper proposes a lightweight yet powerful network structure that consists of the Rapidly Digested Convolutional Layers (RDCL) and the multiple scale convolutional layers (MSCL) to enable FaceBoxes to achieve real-time speed on the CPU.
Journal ArticleDOI
Multi-view subspace clustering with intactness-aware similarity
TL;DR: A novel multi-view subspace clustering model that attempts to form an informative intactness-aware similarity based on the intact space learning technique is proposed and its superior performance over other state-of-the-art alternatives is revealed.
Book ChapterDOI
Standardization of face image sample quality
TL;DR: An approach for standardization of facial image quality is presented, and facial symmetry based methods for its assessment by which facial asymmetries caused by non-frontal lighting and improper facial pose can be measured are developed.