What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Facial recognition system and Feature extraction.
His Artificial intelligence study frequently draws connections to adjacent fields such as Machine learning.
His studies in Pattern recognition integrate themes in fields like Speech recognition, Facial expression and Face.
The various areas that Liming Chen examines in his Computer vision study include Robustness and Pattern recognition.
His Facial recognition system research is multidisciplinary, incorporating perspectives in Video tracking and Mean curvature.
In his research, Feature selection is intimately related to Depth map, which falls under the overarching field of Feature extraction.
His most cited work include:
- Local Binary Patterns and Its Application to Facial Image Analysis: A Survey (643 citations)
- LIRIS-ACCEDE: A Video Database for Affective Content Analysis (127 citations)
- WebGuard: a Web filtering engine combining textual, structural, and visual content-based analysis (109 citations)
What are the main themes of his work throughout his whole career to date?
Liming Chen mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Facial recognition system and Face.
His Machine learning research extends to the thematically linked field of Artificial intelligence.
Liming Chen has included themes like Contextual image classification and Histogram in his Pattern recognition study.
His research related to Face hallucination, Face detection, Video tracking, Local binary patterns and Pixel might be considered part of Computer vision.
His studies deal with areas such as Sparse approximation, Robustness and Image texture as well as Facial recognition system.
His Feature extraction research is multidisciplinary, incorporating elements of Feature and Support vector machine.
He most often published in these fields:
- Artificial intelligence (67.47%)
- Pattern recognition (37.87%)
- Computer vision (37.33%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (67.47%)
- Pattern recognition (37.87%)
- Composite material (3.73%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Composite material, Machine learning and Deep learning.
His work in the fields of Artificial intelligence, such as Facial recognition system, Segmentation, Face and Robustness, intersects with other areas such as Expression.
His Facial recognition system research is classified as research in Computer vision.
Liming Chen combines subjects such as Encoder and Feature with his study of Pattern recognition.
His research in the fields of Artificial neural network overlaps with other disciplines such as Identity.
His Deep learning study integrates concerns from other disciplines, such as Image, Image retrieval, Information retrieval and Scripting language.
Between 2018 and 2021, his most popular works were:
- Improving Shadow Suppression for Illumination Robust Face Recognition (40 citations)
- Deformation behaviors and energy absorption of auxetic lattice cylindrical structures under axial crushing load (20 citations)
- Mechanical properties and energy absorption of 3D printed square hierarchical honeycombs under in-plane axial compression (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Composite material, Artificial intelligence, Energy absorption, Ultimate tensile strength and Fiber.
His Modulus, Elastic modulus and Auxetics study, which is part of a larger body of work in Composite material, is frequently linked to Ring mode, bridging the gap between disciplines.
The various areas that he examines in his Artificial intelligence study include Machine learning and Pattern recognition.
When carried out as part of a general Pattern recognition research project, his work on Discriminative model is frequently linked to work in Expression, therefore connecting diverse disciplines of study.
His work carried out in the field of Ultimate tensile strength brings together such families of science as Volume fraction, Work and Fibre-reinforced plastic.
As a member of one scientific family, Liming Chen mostly works in the field of Fiber, focusing on Stress and, on occasion, Composite number, Compression and Viscoelasticity.
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