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Stan Z. Li
Researcher at Westlake University
Publications - 625
Citations - 49737
Stan Z. Li is an academic researcher from Westlake University. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 97, co-authored 532 publications receiving 41793 citations. Previous affiliations of Stan Z. Li include Microsoft & Macau University of Science and Technology.
Papers
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Proceedings ArticleDOI
When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition
Guosheng Hu,Yongxin Yang,Dong Yi,Josef Kittler,William J. Christmas,Stan Z. Li,Timothy M. Hospedales +6 more
TL;DR: An extensive evaluation of CNN-based face recognition systems (CNN-FRS) and proposes three CNN architectures which are the first reported architectures trained using LFW data to make the work easily reproducible.
Book ChapterDOI
Towards Fast, Accurate and Stable 3D Dense Face Alignment.
TL;DR: A novel regression framework which makes a balance among speed, accuracy and stability, and a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously.
Journal ArticleDOI
Content-based audio classification and segmentation by using support vector machines
TL;DR: Experiments on a database composed of about 4- hour audio data show that the proposed classifier is very efficient on audio classification and segmentation and shows the accuracy of the SVM-based method is much better than the method based on KNN and GMM.
Proceedings ArticleDOI
Face liveness detection with component dependent descriptor
TL;DR: A component-based face coding approach for liveness detection that makes good use of micro differences between genuine faces and fake faces is proposed and can achieve the best liveness Detection performance in three databases.
Posted Content
Convolutional Channel Features
TL;DR: Wang et al. as mentioned in this paper proposed an integrated method called Convolutional Channel Features (CCF), which transferred low-level features from pre-trained CNN models to feed the boosting forest model.