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Shiguang Shan
Researcher at Chinese Academy of Sciences
Publications - 512
Citations - 30066
Shiguang Shan is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 76, co-authored 475 publications receiving 23566 citations. Previous affiliations of Shiguang Shan include University of Maryland, College Park & Media Research Center.
Papers
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Book ChapterDOI
Multi-view discriminant analysis
TL;DR: A Multi-view Discriminant Analysis (MvDA) method, which seeks for a discriminant common space by jointly learning multiple view-specific linear transforms for robust object recognition from multiple views, in a non-pairwise manner is proposed.
Book ChapterDOI
Generative Adversarial Network with Spatial Attention for Face Attribute Editing
TL;DR: This work introduces the spatial attention mechanism into GAN framework (referred to as SaGAN), to only alter the attribute-specific region and keep the rest unchanged, and can achieve promising visual results, and keep those attribute-irrelevant regions unchanged.
Proceedings ArticleDOI
Single-Side Domain Generalization for Face Anti-Spoofing
TL;DR: Zhang et al. as mentioned in this paper proposed an end-to-end single-side domain generalization framework (SSDG) to improve the generalization ability of face anti-spoofing.
Proceedings ArticleDOI
Hierarchical Ensemble of Global and Local Classifiers for Face Recognition
TL;DR: A novel face recognition method which exploits both global and local discriminative features, and which encodes the holistic facial information, such as facial contour, is proposed.
Posted Content
Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach
TL;DR: This paper presents a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image, and introduces an unconstrained face database (LFW+), an extension of public-domain LFW, with heterogeneous demographic attributes obtained via crowdsourcing.