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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
Face recognition using ordinal features
TL;DR: In this article, an ordinal feature based method for face recognition is presented, where ordinal features are used to represent faces and AdaBoost learning is applied to select most effective hamming distance based weak classifiers and build a powerful classifier.
Posted Content
Open-set Person Re-identification
TL;DR: A database collected from a video surveillance setting of 6 cameras, with 200 persons and 7,413 images segmented is presented, and a benchmark protocol for evaluating the performance under the open-set person re-identification scenario is developed.
Journal ArticleDOI
Moving Object Detection Revisited: Speed and Robustness
TL;DR: A new integration framework of texture and color information for background modeling is proposed, in which the foreground decision equation includes three parts (one part for color information, one part for texture information, and the left part for the integration of color and texture information).
Proceedings ArticleDOI
Real-time high performance deformable model for face detection in the wild
TL;DR: By integrating the three techniques, noticeable improvements over previous state-of-the-art on FDDB with real-time speed are demonstrated, under widely comparisons with both academic and commercial detectors.
Proceedings ArticleDOI
Deeply-learned Hybrid Representations for Facial Age Estimation
TL;DR: A novel unified network named Deep Hybrid-Aligned Architecture for facial age estimation that contains global, local and global-local branches, which are jointly optimized and thus can capture multiple types of features with complementary information.