2020 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to automatic facial expression analysis and human activity understanding
2018 - IEEE Fellow For contributions to automatic facial expression analysis and human activity understanding
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Facial expression and Facial recognition system. As part of his studies on Artificial intelligence, he frequently links adjacent subjects like Identification. His Pattern recognition research is multidisciplinary, relying on both Text mining, String and Contextual image classification.
His Facial expression study integrates concerns from other disciplines, such as Orientation, Relation, Feature extraction and Face. His work focuses on many connections between Object detection and other disciplines, such as Background subtraction, that overlap with his field of interest in Robustness. His work is dedicated to discovering how Facial Action Coding System, Speech recognition are connected with Surprise and Feature detection and other disciplines.
Yingli Tian spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Object detection. His Artificial intelligence study frequently draws connections to other fields, such as Machine learning. All of his Computer vision and Object, Background subtraction, Motion, Tracking and Video tracking investigations are sub-components of the entire Computer vision study.
The concepts of his Pattern recognition study are interwoven with issues in Artificial neural network, Histogram, Deep learning and Pyramid. His Feature extraction study which covers Facial recognition system that intersects with Facial Action Coding System. Yingli Tian interconnects Speech recognition, Face and Gesture in the investigation of issues within Facial expression.
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Deep learning, Computer vision and Point cloud. The study of Artificial intelligence is intertwined with the study of Natural language processing in a number of ways. His Pattern recognition research incorporates themes from Self supervised learning, Feature and Contrast.
His Deep learning study combines topics in areas such as Scatterometer, Sea ice and Atmospheric model. His study ties his expertise on Ground-penetrating radar together with the subject of Computer vision. His work investigates the relationship between Point cloud and topics such as Rendering that intersect with problems in Pyramid, Scale, Solid modeling and Depth map.
His primary areas of investigation include Artificial intelligence, Deep learning, Pattern recognition, Artificial neural network and Convolutional neural network. His Artificial intelligence study frequently involves adjacent topics like Computer vision. His work carried out in the field of Computer vision brings together such families of science as Word and Facial expression.
His Feature extraction, Multiclass classification and Euclidean distance study in the realm of Pattern recognition interacts with subjects such as Ambiguity. He combines subjects such as Pascal, Segmentation, Image segmentation and Gesture with his study of Convolutional neural network. His work on Pyramid as part of general Feature study is frequently linked to Pulmonary nodule and Nodule detection, therefore connecting diverse disciplines of science.
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.
Comprehensive database for facial expression analysis
T. Kanade;J.F. Cohn;Yingli Tian.
ieee international conference on automatic face and gesture recognition (2000)
Recognizing action units for facial expression analysis
Y.-I. Tian;T. Kanade;J.F. Cohn.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2001)
Recognizing actions using depth motion maps-based histograms of oriented gradients
Xiaodong Yang;Chenyang Zhang;YingLi Tian.
acm multimedia (2012)
EigenJoints-based action recognition using Naïve-Bayes-Nearest-Neighbor
Xiaodong Yang;Ying Li Tian.
computer vision and pattern recognition (2012)
Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
Longlong Jing;Yingli Tian.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)
Appearance models for occlusion handling
Andrew W. Senior;Arun Hampapur;Ying-li Tian;Lisa M. Brown.
Image and Vision Computing (2006)
Text String Detection From Natural Scenes by Structure-Based Partition and Grouping
Chucai Yi;YingLi Tian.
IEEE Transactions on Image Processing (2011)
Super Normal Vector for Activity Recognition Using Depth Sequences
Xiaodong Yang;YingLi Tian.
computer vision and pattern recognition (2014)
Robust and efficient foreground analysis for real-time video surveillance
Ying-Li Tian;M. Lu;A. Hampapur.
computer vision and pattern recognition (2005)
Effective 3D action recognition using EigenJoints
Xiaodong Yang;YingLi Tian.
Journal of Visual Communication and Image Representation (2014)
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: