What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Xilin Chen mainly investigates Artificial intelligence, Pattern recognition, Facial recognition system, Computer vision and Face.
As part of his studies on Artificial intelligence, Xilin Chen often connects relevant areas like Machine learning.
His Pattern recognition study combines topics from a wide range of disciplines, such as Contextual image classification, Feature, Rayleigh quotient and Sample.
In general Facial recognition system, his work in Face detection is often linked to Gabor wavelet linking many areas of study.
His study in Computer vision is interdisciplinary in nature, drawing from both Encoding and Pattern recognition.
His studies deal with areas such as Similarity, Text mining, Data mining, Database and Pose as well as Face.
His most cited work include:
- Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition (911 citations)
- WLD: A Robust Local Image Descriptor (784 citations)
- The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations (772 citations)
What are the main themes of his work throughout his whole career to date?
Xilin Chen spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Facial recognition system and Face.
His research links Machine learning with Artificial intelligence.
His studies in Pattern recognition integrate themes in fields like Contextual image classification, Object detection, Histogram and Robustness.
Computer vision is closely attributed to Representation in his research.
The concepts of his Facial recognition system study are interwoven with issues in Classifier, Subspace topology, Facial expression and Training set.
Xilin Chen integrates several fields in his works, including Face, Generalization and Focus.
He most often published in these fields:
- Artificial intelligence (90.17%)
- Pattern recognition (49.33%)
- Computer vision (38.15%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (90.17%)
- Pattern recognition (49.33%)
- Computer vision (38.15%)
In recent papers he was focusing on the following fields of study:
Xilin Chen mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Face and Discriminative model.
While the research belongs to areas of Artificial intelligence, Xilin Chen spends his time largely on the problem of Machine learning, intersecting his research to questions surrounding Class.
His work in Pattern recognition covers topics such as Binary code which are related to areas like Image retrieval.
In Computer vision, Xilin Chen works on issues like Relation, which are connected to Representation.
His Face research is multidisciplinary, incorporating perspectives in End-to-end principle and Image.
The Discriminative model study combines topics in areas such as Feature and Deep learning.
Between 2018 and 2021, his most popular works were:
- AttGAN: Facial Attribute Editing by Only Changing What You Want (224 citations)
- Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism (141 citations)
- Interaction-And-Aggregation Network for Person Re-Identification (81 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Face and Feature extraction.
His Feature, Discriminative model, Facial recognition system and Convolutional neural network study in the realm of Artificial intelligence connects with subjects such as Task analysis.
Within one scientific family, he focuses on topics pertaining to Feature learning under Facial recognition system, and may sometimes address concerns connected to Gesture and Autoencoder.
His Pattern recognition course of study focuses on Encoder and Language model and Transformer.
His Computer vision research incorporates elements of Identification and Code.
Xilin Chen has included themes like Normalization, Scene graph and Image retrieval in his Feature extraction study.
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.
