What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Di Huang focuses on Artificial intelligence, Pattern recognition, Computer vision, Facial recognition system and Face.
Her biological study focuses on Gesture.
Her study in Pattern recognition is interdisciplinary in nature, drawing from both Artificial neural network and Robustness.
Her Computer vision research includes themes of Discriminative model and Pattern recognition.
The Facial recognition system study combines topics in areas such as Facial expression and Pyramid.
Her studies in Face integrate themes in fields like Affective computing, Feature, State and Categorization.
Her most cited work include:
- Local Binary Patterns and Its Application to Facial Image Analysis: A Survey (643 citations)
- Receptive Field Block Net for Accurate and Fast Object Detection (293 citations)
- Towards 3D Face Recognition in the Real: A Registration-Free Approach Using Fine-Grained Matching of 3D Keypoint Descriptors (100 citations)
What are the main themes of her work throughout her whole career to date?
Di Huang mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Facial recognition system and Face.
Her research related to Feature extraction, Discriminative model, Robustness, Local binary patterns and Deep learning might be considered part of Artificial intelligence.
Her research in Pattern recognition intersects with topics in Representation, Histogram, Facial expression and Feature.
Di Huang regularly links together related areas like Identification in her Computer vision studies.
Di Huang has included themes like Contextual image classification, Pose and Image fusion in her Facial recognition system study.
Her work carried out in the field of Face brings together such families of science as Pyramid and Solid modeling.
She most often published in these fields:
- Artificial intelligence (90.20%)
- Pattern recognition (61.44%)
- Computer vision (49.02%)
What were the highlights of her more recent work (between 2019-2021)?
- Artificial intelligence (90.20%)
- Pattern recognition (61.44%)
- Computer vision (49.02%)
In recent papers she was focusing on the following fields of study:
Her primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Object detection and Feature extraction.
Her study on Face, Deep learning and Discriminative model is often connected to Domain as part of broader study in Artificial intelligence.
Her Facial recognition system study in the realm of Face interacts with subjects such as Space.
Her Pattern recognition research is multidisciplinary, incorporating perspectives in Facial expression recognition, Feature, Pyramid, Generative adversarial network and Rgb image.
Her work on Orientation and Iterative reconstruction as part of general Computer vision study is frequently connected to Optical illusion and Focus, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Her Feature extraction research is multidisciplinary, incorporating elements of Binary pattern, Representation, Histogram, Discriminant and Pattern recognition.
Between 2019 and 2021, her most popular works were:
- Local Discriminant Direction Binary Pattern for Palmprint Representation and Recognition (29 citations)
- Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation (16 citations)
- Pay Attention to Them: Deep Reinforcement Learning-Based Cascade Object Detection (14 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Her scientific interests lie mostly in Artificial intelligence, Pattern recognition, Object detection, Feature extraction and Face.
Artificial intelligence and Computer vision are commonly linked in her work.
Her Image resolution study, which is part of a larger body of work in Computer vision, is frequently linked to Detector, bridging the gap between disciplines.
In general Pattern recognition, her work in Feature vector is often linked to Domain linking many areas of study.
Her research in Feature extraction intersects with topics in Representation, Histogram, Discriminant, Discriminative model and Feature selection.
The Facial recognition system and Age progression research Di Huang does as part of her general Face study is frequently linked to other disciplines of science, such as Space, Generator and Identity, therefore creating a link between diverse domains 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.
