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
Maximum Correntropy Criterion for Robust Face Recognition
Ran He,Wei-Shi Zheng,Bao-Gang Hu +2 more
TL;DR: The proposed sparse correntropy framework is more robust and efficient in dealing with the occlusion and corruption problems in face recognition as compared to the related state-of-the-art methods and the computational cost is much lower than the SRC algorithms.
Journal ArticleDOI
A Light CNN for Deep Face Representation With Noisy Labels
TL;DR: Experimental results show that the proposed framework can utilize large-scale noisy data to learn a Light model that is efficient in computational costs and storage spaces and achieves state-of-the-art results on various face benchmarks without fine-tuning.
Proceedings ArticleDOI
Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis
TL;DR: Tang et al. as discussed by the authors proposed a Two-Pathway Generative Adversarial Network (TP-GAN) for photorealistic frontal view synthesis by simultaneously perceiving global structures and local details.
Proceedings ArticleDOI
Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution
TL;DR: A wavelet-based CNN approach that can ultra-resolve a very low resolution face image of 16 × 16 or smaller pixelsize to its larger version of multiple scaling factors in a unified framework with three types of loss: wavelet prediction loss, texture loss and full-image loss is presented.
Journal ArticleDOI
Robust Principal Component Analysis Based on Maximum Correntropy Criterion
TL;DR: Numerical results demonstrate that the proposed method can outperform robust rotational-invariant PCAs based on L1 norm when outliers occur and requires no assumption about the zero-mean of data for processing and can estimate data mean during optimization.