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
More filters
Journal ArticleDOI
Joint Feature Selection and Subspace Learning for Cross-Modal Retrieval
TL;DR: An iterative algorithm is presented to solve the proposed joint learning problem, along with its convergence analysis, and Experimental results on cross-modal retrieval tasks demonstrate that the proposed method outperforms the state-of-the-art subspace approaches.
Proceedings ArticleDOI
Robust view transformation model for gait recognition
TL;DR: Results show that the proposed method outperforms the other existing methods and brings the advantages that the view transformation model is robust to viewing angle variation, clothing and carrying condition changes.
Journal ArticleDOI
Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation
TL;DR: A half-quadratic (HQ) framework to solve the robust sparse representation problem is developed and it is shown that the ℓ1-regularization solved by soft-thresholding function has a dual relationship to Huber M-estimator, which theoretically guarantees the performance of robust sparse Representation in terms of M-ESTimation.
Proceedings ArticleDOI
Learning Coupled Feature Spaces for Cross-Modal Matching
TL;DR: A novel coupled linear regression framework to deal with the measure of relevance and coupled feature selection in cross-modal data matching, and an iterative algorithm based on half-quadratic minimization to solve the proposed regularized linear regression problem.
Journal ArticleDOI
Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition
TL;DR: Wasserstein convolutional neural network (WCNN) as discussed by the authors was proposed to learn invariant features between near-infrared (NIR) and visual (VIS) face images, and the Wasserstein distance was introduced into the NIR-VIS shared layer to measure the dissimilarity between heterogeneous feature distributions.