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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
Exclusivity-Consistency Regularized Multi-view Subspace Clustering
TL;DR: A novel multi-view subspace clustering model that attempts to harness the complementary information between different representations by introducing a novel position-aware exclusivity term and a consistency term is employed to make these complementary representations to further have a common indicator.
Journal ArticleDOI
Face Recognition by Exploring Information Jointly in Space, Scale and Orientation
TL;DR: A novel face representation and recognition approach by exploring information jointly in image space, scale and orientation domains by convolving multiscale and multi-orientation Gabor filters is proposed.
Journal ArticleDOI
Ensemble-based discriminant learning with boosting for face recognition
TL;DR: A novel weakness analysis theory is developed that attempts to boost a strong learner by increasing the diversity between the classifiers created by the learner, at the expense of decreasing their margins, so as to achieve a tradeoff suggested by recent boosting studies for a low generalization error.
Proceedings ArticleDOI
Face liveness detection by learning multispectral reflectance distributions
TL;DR: A multispectral face liveness detection method that is user cooperation free, adaptive to various user-system distances, and has the following advantages: (a) the requirement on the users' cooperation is no longer needed, making the liveness Detection user friendly and fast.
Proceedings ArticleDOI
Multiple Target Tracking Based on Undirected Hierarchical Relation Hypergraph
TL;DR: A novel data association approach based on undirected hierarchical relation hypergraph is proposed, which formulates the tracking task as a hierarchical dense neighborhoods searching problem on the dynamically constructed Undirected affinity graph and makes the tracker robust to the spatially close targets with similar appearance.