What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Pattern recognition
His primary areas of investigation include Artificial intelligence, Facial recognition system, Machine learning, Deep learning and Pattern recognition.
Feature extraction, Discriminative model, Face, Artificial neural network and Biometrics are subfields of Artificial intelligence in which his conducts study.
The Discriminative model study combines topics in areas such as Image, Facial expression and Feature.
His Face research is multidisciplinary, incorporating perspectives in Relation and Convolutional neural network.
The various areas that he examines in his Machine learning study include Contextual image classification, Object detection and Embedding.
In his study, Support vector machine is inextricably linked to Computer vision, which falls within the broad field of Pattern recognition.
His most cited work include:
- Deep visual domain adaptation: A survey (505 citations)
- Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary (456 citations)
- Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild (268 citations)
What are the main themes of his work throughout his whole career to date?
Weihong Deng mostly deals with Artificial intelligence, Pattern recognition, Facial recognition system, Discriminative model and Machine learning.
Many of his studies involve connections with topics such as Computer vision and Artificial intelligence.
He has included themes like Artificial neural network, Image and Facial expression in his Pattern recognition study.
His Facial recognition system research is multidisciplinary, incorporating elements of Feature, Principal component analysis, Robustness and Biometrics.
His Discriminative model research integrates issues from Margin, Embedding, Feature learning and Softmax function.
His work on Overfitting as part of general Machine learning research is often related to Process, thus linking different fields of science.
He most often published in these fields:
- Artificial intelligence (93.37%)
- Pattern recognition (60.77%)
- Facial recognition system (48.07%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (93.37%)
- Pattern recognition (60.77%)
- Facial recognition system (48.07%)
In recent papers he was focusing on the following fields of study:
Weihong Deng mainly investigates Artificial intelligence, Pattern recognition, Facial recognition system, Machine learning and Face.
His study in Discriminative model, Deep learning, Convolutional neural network, Artificial neural network and Feature is carried out as part of his Artificial intelligence studies.
His Pattern recognition research is multidisciplinary, relying on both Image and Noise.
The concepts of his Facial recognition system study are interwoven with issues in Feature extraction and Overfitting.
His Machine learning research incorporates themes from Adversarial system, Sample, Facial expression recognition and Robustness.
His Face course of study focuses on Margin and Reinforcement learning.
Between 2019 and 2021, his most popular works were:
- Deep Facial Expression Recognition: A Survey (234 citations)
- Deep face recognition: A survey (98 citations)
- Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Facial recognition system, Pattern recognition, Deep learning and Face.
His Artificial intelligence research incorporates elements of Margin and Machine learning.
The Machine learning study combines topics in areas such as Normalization, Pixel, Feature extraction and DeepFace.
His Facial recognition system study frequently links to adjacent areas such as Skewness.
Weihong Deng combines subjects such as Adversarial system, Field, Convolutional neural network and Robustness with his study of Deep learning.
His Face study incorporates themes from Image quality and Key.
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