What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Chengjun Liu mostly deals with Artificial intelligence, Pattern recognition, Facial recognition system, Linear discriminant analysis and Computer vision.
His study in Artificial intelligence focuses on Eigenface, Feature vector, Feature extraction, FERET database and Gabor wavelet.
His study looks at the relationship between Feature vector and fields such as Face, as well as how they intersect with chemical problems.
His biological study spans a wide range of topics, including Image processing and Face Recognition Grand Challenge.
The study incorporates disciplines such as Automatic indexing, Independent component analysis and Bayes classifier in addition to Facial recognition system.
His study in Linear discriminant analysis is interdisciplinary in nature, drawing from both Empirical risk minimization, Database index, Data mining, Database and Fitness function.
His most cited work include:
- Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition (1635 citations)
- Gabor-based kernel PCA with fractional power polynomial models for face recognition (522 citations)
- Independent component analysis of Gabor features for face recognition (425 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Facial recognition system and Feature extraction.
His work in Face Recognition Grand Challenge, Color space, Contextual image classification, Linear discriminant analysis and Color histogram are all subfields of Artificial intelligence research.
Chengjun Liu interconnects Local binary patterns, Face and Feature in the investigation of issues within Pattern recognition.
His Facial recognition system research integrates issues from Image processing and Pattern recognition.
The study incorporates disciplines such as Visualization, Sparse approximation and Haar-like features in addition to Feature extraction.
His Feature vector study combines topics in areas such as Classification rule and Gabor wavelet.
He most often published in these fields:
- Artificial intelligence (96.69%)
- Pattern recognition (79.34%)
- Computer vision (57.02%)
What were the highlights of his more recent work (between 2014-2020)?
- Artificial intelligence (96.69%)
- Pattern recognition (79.34%)
- Computer vision (57.02%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Sparse approximation and Feature.
Artificial intelligence connects with themes related to Machine learning in his study.
His work carried out in the field of Machine learning brings together such families of science as Knn classifier, Discriminant and Linear discriminant analysis.
His Contextual image classification research extends to Pattern recognition, which is thematically connected.
His Feature extraction research includes themes of Visualization, Principal component analysis and Fisher kernel.
His Classifier research is multidisciplinary, incorporating perspectives in Facial recognition system and k-nearest neighbors algorithm.
Between 2014 and 2020, his most popular works were:
- Eye detection using discriminatory Haar features and a new efficient SVM (43 citations)
- A novel locally linear KNN model for visual recognition (28 citations)
- A Novel Locally Linear KNN Method With Applications to Visual Recognition (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Feature extraction, Feature and Facial recognition system.
His research investigates the connection with Artificial intelligence and areas like Computer vision which intersect with concerns in Principal component analysis.
His study of Fisher kernel is a part of Pattern recognition.
His Fisher kernel study incorporates themes from Mixture model, Discriminant, Cosine similarity and Linear discriminant analysis.
His work is dedicated to discovering how Feature, Color space are connected with Similarity measure, RGB color model, Pixel, Margin and RGB color space and other disciplines.
His Facial recognition system study frequently links to other fields, such as Visual recognition.
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