Wenming Zheng focuses on Artificial intelligence, Pattern recognition, Facial recognition system, Discriminative model and Feature extraction. In his work, Feature selection is strongly intertwined with Speech recognition, which is a subfield of Artificial intelligence. His work focuses on many connections between Pattern recognition and other disciplines, such as Computer vision, that overlap with his field of interest in Mixture model.
As part of the same scientific family, Wenming Zheng usually focuses on Facial recognition system, concentrating on Histogram and intersecting with Kernel, Image texture and Multiple kernel learning. His study in Discriminative model is interdisciplinary in nature, drawing from both Adjacency matrix, Local binary patterns and Feature learning. His research investigates the link between Feature extraction and topics such as Feature that cross with problems in Data set.
Wenming Zheng spends much of his time researching Artificial intelligence, Pattern recognition, Speech recognition, Feature extraction and Discriminative model. His research on Artificial intelligence frequently connects to adjacent areas such as Computer vision. His research investigates the connection between Pattern recognition and topics such as Face that intersect with problems in Image.
His work on Emotion recognition and Speaker recognition as part of general Speech recognition research is frequently linked to Novelty and Test data, bridging the gap between disciplines. He focuses mostly in the field of Feature extraction, narrowing it down to matters related to Kernel and, in some cases, Kernel. His Facial expression study combines topics in areas such as Feature learning and Convolutional neural network.
Wenming Zheng mainly focuses on Artificial intelligence, Pattern recognition, Discriminative model, Emotion recognition and Focus. His Artificial intelligence research includes elements of Machine learning and Brain–computer interface. His studies deal with areas such as Facial recognition system and Microexpression as well as Machine learning.
His Pattern recognition study combines topics from a wide range of disciplines, such as Artificial neural network, Feature, Representation and Electroencephalography. In his study, which falls under the umbrella issue of Discriminative model, Emotion classification and Feature selection is strongly linked to Feature extraction. His work carried out in the field of Emotion recognition brings together such families of science as Eeg electrodes and Convolution.
His main research concerns Artificial intelligence, Discriminative model, Feature extraction, Speech recognition and Convolutional neural network. The study incorporates disciplines such as Machine learning and Pattern recognition in addition to Artificial intelligence. The Machine learning study combines topics in areas such as Facial recognition system and Microexpression.
His Pattern recognition research integrates issues from Artificial neural network and Feature. His studies in Discriminative model integrate themes in fields like Emotion classification and Feature selection. Wenming Zheng combines subjects such as Mixture model, Deep learning, Theoretical computer science and Vertex with his study of Convolutional neural network.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Facial expression recognition using kernel canonical correlation analysis (KCCA)
Wenming Zheng;Xiaoyan Zhou;Cairong Zou;Li Zhao.
IEEE Transactions on Neural Networks (2006)
A Deep Neural Network-Driven Feature Learning Method for Multi-view Facial Expression Recognition
Tong Zhang;Wenming Zheng;Zhen Cui;Yuan Zong.
IEEE Transactions on Multimedia (2016)
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
Tengfei Song;Wenming Zheng;Peng Song;Zhen Cui.
IEEE Transactions on Affective Computing (2020)
Spatial–Temporal Recurrent Neural Network for Emotion Recognition
Tong Zhang;Wenming Zheng;Zhen Cui;Yuan Zong.
IEEE Transactions on Systems, Man, and Cybernetics (2019)
Fisher Discriminant Analysis With L1-Norm
Haixian Wang;Xuesong Lu;Zilan Hu;Wenming Zheng.
IEEE Transactions on Systems, Man, and Cybernetics (2014)
Gender classification based on boosting local binary pattern
Ning Sun;Wenming Zheng;Changyin Sun;Cairong Zou.
international symposium on neural networks (2006)
Spontaneous facial micro-expression analysis using Spatiotemporal Completed Local Quantized Patterns
Xiaohua Huang;Guoying Zhao;Xiaopeng Hong;Wenming Zheng.
L1-Norm-Based Common Spatial Patterns
Haixian Wang;Qin Tang;Wenming Zheng.
IEEE Transactions on Biomedical Engineering (2012)
Locally nearest neighbor classifiers for pattern classification
Wenming Zheng;Li Zhao;Cairong Zou.
Pattern Recognition (2004)
Multi-View Facial Expression Recognition Based on Group Sparse Reduced-Rank Regression
IEEE Transactions on Affective Computing (2014)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: