R
Ran He
Researcher at Chinese Academy of Sciences
Publications - 330
Citations - 11787
Ran He 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 47, co-authored 303 publications receiving 8707 citations. Previous affiliations of Ran He include Dalian University of Technology & Nanyang Technological University.
Papers
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Journal ArticleDOI
Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition
TL;DR: A significant reduction of computational costs is reached in comparison with sparse representation classifier; this enables the TSR to be more suitable for robust face recognition on a large-scale dataset.
Proceedings Article
Deep Supervised Discrete Hashing
TL;DR: Wang et al. as discussed by the authors developed a deep supervised discrete hashing algorithm based on the assumption that the learned binary codes should be ideal for classification, where both the pairwise label information and the classification information are used to learn the hash codes within one stream framework.
Journal ArticleDOI
A regularized correntropy framework for robust pattern recognition
TL;DR: A new multiple linear regression model using regularized correntropy to improve the robustness of the classical mean square error (MSE) criterion that is sensitive to outliers is proposed and a novel algorithm to solve the nonlinear optimization problem is proposed.
Book ChapterDOI
MEAD: A Large-Scale Audio-Visual Dataset for Emotional Talking-Face Generation
Kaisiyuan Wang,Qianyi Wu,Linsen Song,Linsen Song,Zhuoqian Yang,Wayne Wu,Chen Qian,Ran He,Yu Qiao,Chen Change Loy +9 more
TL;DR: The Multi-view Emotional Audio-visual Dataset (MEAD) is built, a talking-face video corpus featuring 60 actors and actresses talking with eight different emotions at three different intensity levels that could benefit a number of different research fields including conditional generation, cross-modal understanding and expression recognition.
Journal ArticleDOI
Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation
TL;DR: This paper seeks the optimal projection via a novel relaxed domain-irrelevant clustering-promoting term that jointly bridges the cross-domain semantic gap and increases the intra-class compactness in both domains.