Z
Zhaohui Wu
Researcher at Zhejiang University
Publications - 427
Citations - 10742
Zhaohui Wu is an academic researcher from Zhejiang University. The author has contributed to research in topics: Web service & Semantic Web. The author has an hindex of 49, co-authored 426 publications receiving 9341 citations.
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Proceedings ArticleDOI
Eyeblink-based Anti-Spoofing in Face Recognition from a Generic Webcamera
TL;DR: A real-time liveness detection approach against photograph spoofing in face recognition, by recognizing spontaneous eyeblinks, which is a non-intrusive manner, which outperforms the cascaded Adaboost and HMM in task of eyeblink detection.
Journal ArticleDOI
Constrained Nonnegative Matrix Factorization for Image Representation
TL;DR: It is shown how explicitly combining label information improves the discriminating power of the resulting matrix decomposition, and the effectiveness of the novel algorithm in comparison to the state-of-the-art approaches through a set of evaluations based on real-world applications.
Journal ArticleDOI
Prediction of urban human mobility using large-scale taxi traces and its applications
Xiaolong Li,Gang Pan,Zhaohui Wu,Guande Qi,Shijian Li,Daqing Zhang,Wangsheng Zhang,Zonghui Wang +7 more
TL;DR: An improved ARIMA based prediction method to forecast the spatial-temporal variation of passengers in a hotspot and the application of the prediction approach to help drivers find their next passengers is proposed.
Journal ArticleDOI
Land-Use Classification Using Taxi GPS Traces
TL;DR: It is found that pick-up/set-down dynamics, extracted from taxi traces, exhibited clear patterns corresponding to the land-use classes of these regions, particularly for recognizing the social function of urban land by using one year's trace data from 4000 taxis.
Journal ArticleDOI
On Deep Learning for Trust-Aware Recommendations in Social Networks
TL;DR: A two-phase recommendation process is proposed to utilize deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user’s trusted friendships.