What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Local binary patterns and Facial recognition system.
Feature extraction, Robustness, Histogram, Face and Image texture are subfields of Artificial intelligence in which his conducts study.
In the field of Pattern recognition, his study on Sparse approximation overlaps with subjects such as Path graph.
His study in the field of Background subtraction and Biometrics is also linked to topics like Heart rate measurement and Heart rate monitoring.
His Local binary patterns research incorporates themes from Segmentation, Discriminative model and Invariant.
His Facial recognition system research is multidisciplinary, incorporating elements of Margin, Feature, Facial expression and Spotting.
His most cited work include:
- Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions (2013 citations)
- WLD: A Robust Local Image Descriptor (784 citations)
- Computer Vision Using Local Binary Patterns (393 citations)
What are the main themes of his work throughout his whole career to date?
Guoying Zhao mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Local binary patterns and Facial recognition system.
His Artificial intelligence study focuses mostly on Face, Feature extraction, Discriminative model, Facial expression and Deep learning.
His Feature extraction research integrates issues from Contextual image classification, Feature selection, Three-dimensional face recognition and Feature vector.
Guoying Zhao interconnects Facial expression recognition, Spotting, Speech recognition, Support vector machine and Face hallucination in the investigation of issues within Facial expression.
His research in Pattern recognition tackles topics such as Histogram which are related to areas like Image texture.
His work on Anti spoofing and Face detection is typically connected to Spoofing attack as part of general Facial recognition system study, connecting several disciplines of science.
He most often published in these fields:
- Artificial intelligence (81.85%)
- Pattern recognition (53.08%)
- Computer vision (33.90%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (81.85%)
- Pattern recognition (53.08%)
- Benchmark (10.62%)
In recent papers he was focusing on the following fields of study:
Guoying Zhao mainly focuses on Artificial intelligence, Pattern recognition, Benchmark, Facial recognition system and Face.
His Machine learning research extends to Artificial intelligence, which is thematically connected.
His research integrates issues of RGB color model, Photoplethysmogram, Noise and Skeleton in his study of Pattern recognition.
His research on Benchmark also deals with topics like
- Feature which connect with Valence and F1 score,
- Task, Database, Pixel and Liveness most often made with reference to Representation.
His study in the field of Anti spoofing also crosses realms of Spoofing attack.
His work in Face tackles topics such as Feature learning which are related to areas like Hyperbolic space.
Between 2019 and 2021, his most popular works were:
- Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching (50 citations)
- Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-Expressions (43 citations)
- Searching Central Difference Convolutional Networks for Face Anti-Spoofing (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of study are Artificial intelligence, Pattern recognition, Benchmark, Facial recognition system and Face.
His work on MNIST database as part of general Artificial intelligence research is frequently linked to Probability distribution, thereby connecting diverse disciplines of science.
His Feature extraction, Anomaly detection and Classifier study in the realm of Pattern recognition interacts with subjects such as Novelty.
His Feature extraction study incorporates themes from Representation, Histogram, Deep learning and Data modeling.
Guoying Zhao usually deals with Benchmark and limits it to topics linked to Feature and Facial expression recognition, Discriminative model and Database.
His biological study spans a wide range of topics, including Machine learning and Finite difference.
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