H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 49 Citations 7,594 255 World Ranking 3001 National Ranking 63

Overview

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.

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.

Top Publications

Local Binary Patterns and Its Application to Facial Image Analysis: A Survey

Di Huang;Caifeng Shan;M. Ardabilian;Yunhong Wang.
systems man and cybernetics (2011)

999 Citations

LIRIS-ACCEDE: A Video Database for Affective Content Analysis

Yoann Baveye;Emmanuel Dellandrea;Christel Chamaret;Liming Chen.
IEEE Transactions on Affective Computing (2015)

211 Citations

WebGuard: a Web filtering engine combining textual, structural, and visual content-based analysis

M. Hammami;Y. Chahir;L. Chen.
IEEE Transactions on Knowledge and Data Engineering (2006)

166 Citations

A robust agorithm for eye detection on gray intensity face without spectacles

Kun Peng;Liming Chen;Su Ruan;Georgy Kukharev.
Journal of Computer Science and Technology (2005)

163 Citations

Image region description using orthogonal combination of local binary patterns enhanced with color information

Chao Zhu;Charles-Edmond Bichot;Liming Chen.
Pattern Recognition (2013)

147 Citations

A coarse-to-fine curvature analysis-based rotation invariant 3D face landmarking

Przemyslaw Szeptycki;Mohsen Ardabilian;Liming Chen.
international conference on biometrics theory applications and systems (2009)

143 Citations

Multi-scale Color Local Binary Patterns for Visual Object Classes Recognition

Chao Zhu;Charles-Edmond Bichot;Liming Chen.
international conference on pattern recognition (2010)

133 Citations

Gender identification using a general audio classifier

H. Harb;Liming Chen.
international conference on multimedia and expo (2003)

132 Citations

Multimodal 2D+3D Facial Expression Recognition With Deep Fusion Convolutional Neural Network

Huibin Li;Jian Sun;Zongben Xu;Liming Chen.
IEEE Transactions on Multimedia (2017)

129 Citations

3-D Face Recognition Using eLBP-Based Facial Description and Local Feature Hybrid Matching

Di Huang;M. Ardabilian;Yunhong Wang;Liming Chen.
IEEE Transactions on Information Forensics and Security (2012)

128 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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