S
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.
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Proceedings ArticleDOI
Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition
TL;DR: A novel non-statistics based face representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the face model, so that the generalizability problem is naturally avoided.
Journal ArticleDOI
WLD: A Robust Local Image Descriptor
TL;DR: Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT), and experimental results on human face detection also show a promising performance comparable to the best known results onThe MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set.
Journal ArticleDOI
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
TL;DR: The evaluation protocol based on the CAS-PEAL-R1 database is discussed and the performance of four algorithms are presented as a baseline to do the following: elementarily assess the difficulty of the database for face recognition algorithms; preference evaluation results for researchers using the database; and identify the strengths and weaknesses of the commonly used algorithms.
Proceedings ArticleDOI
Deep Supervised Hashing for Fast Image Retrieval
TL;DR: A novel Deep Supervised Hashing method to learn compact similarity-preserving binary code for the huge body of image data and extensive experiments show the promising performance of the method compared with the state-of-the-arts.
Journal ArticleDOI
AttGAN: Facial Attribute Editing by Only Changing What You Want
TL;DR: The proposed method is extended for attribute style manipulation in an unsupervised manner and outperforms the state-of-the-art on realistic attribute editing with other facial details well preserved.