D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 45 Citations 7,421 236 World Ranking 3640 National Ranking 337

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Facial recognition system and Face. In most of his Artificial intelligence studies, his work intersects topics such as Point. Kin-Man Lam has researched Pattern recognition in several fields, including Weighting, Representation, Edge detection, Orientation and Three-dimensional face recognition.

The concepts of his Facial recognition system study are interwoven with issues in Similarity, Feature, Image processing, Feature vector and Principal component analysis. Kin-Man Lam combines subjects such as Process and Histogram matching with his study of Face. His Feature extraction study combines topics in areas such as Cognitive neuroscience of visual object recognition, Face detection, Perspective, Wavelet transform and Pattern recognition.

His most cited work include:

  • Locating and extracting the eye in human face images (298 citations)
  • An analytic-to-holistic approach for face recognition based on a single frontal view (240 citations)
  • A Level Set Approach to Image Segmentation With Intensity Inhomogeneity (237 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Facial recognition system and Face. His is involved in several facets of Artificial intelligence study, as is seen by his studies on Feature extraction, Image, Feature, Face hallucination and Image processing. His Computer vision study frequently draws connections to adjacent fields such as Algorithm.

As a member of one scientific family, Kin-Man Lam mostly works in the field of Pattern recognition, focusing on Image resolution and, on occasion, Convolutional neural network. His Facial recognition system research incorporates elements of Biometrics, Hausdorff distance, Similarity and Pattern recognition. He has included themes like Process and Iterative reconstruction in his Face study.

He most often published in these fields:

  • Artificial intelligence (80.62%)
  • Computer vision (51.08%)
  • Pattern recognition (50.77%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (80.62%)
  • Pattern recognition (50.77%)
  • Computer vision (51.08%)

In recent papers he was focusing on the following fields of study:

Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Generalization are his primary areas of study. His is doing research in Discriminative model, Feature extraction, Deep learning, Feature and Benchmark, both of which are found in Artificial intelligence. His Pattern recognition research includes themes of Facial recognition system, Image, Prior probability and Subspace topology.

His Facial recognition system research is multidisciplinary, relying on both Codebook, Feature and Neural coding. His research brings together the fields of Residual and Computer vision. His work carried out in the field of Convolutional neural network brings together such families of science as Image resolution, Noise measurement, Face and Superresolution.

Between 2018 and 2021, his most popular works were:

  • The extended marine underwater environment database and baseline evaluations (18 citations)
  • Deep-feature encoding-based discriminative model for age-invariant face recognition (15 citations)
  • An Effective Subsuperpixel-Based Approach for Background Subtraction (11 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Machine learning

Kin-Man Lam mainly focuses on Artificial intelligence, Pattern recognition, Feature extraction, Facial recognition system and Discriminative model. His Artificial intelligence study frequently draws parallels with other fields, such as Computer vision. Kin-Man Lam interconnects Basis, Subspace topology, Image denoising and Benchmark in the investigation of issues within Pattern recognition.

His Feature extraction research is multidisciplinary, incorporating elements of Object detection, Representation, Cluster analysis and Feature vector. Kin-Man Lam works mostly in the field of Facial recognition system, limiting it down to topics relating to Deep learning and, in certain cases, Codebook, Face hallucination and Face. His work deals with themes such as Feature, Random forest, Principal component analysis and Image, which intersect with Discriminative model.

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.

Best Publications

Locating and extracting the eye in human face images

Kin-Man Lam;Hong Yan.
Pattern Recognition (1996)

502 Citations

An analytic-to-holistic approach for face recognition based on a single frontal view

Kin-Man Lam;Hong Yan.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1998)

362 Citations

A Level Set Approach to Image Segmentation With Intensity Inhomogeneity

Kaihua Zhang;Lei Zhang;Kin-Man Lam;David Zhang.
IEEE Transactions on Systems, Man, and Cybernetics (2016)

334 Citations

An efficient algorithm for human face detection and facial feature extraction under different conditions

Kwok-Wai Wong;Kin-Man Lam;Wan-Chi Siu.
Pattern Recognition (2001)

233 Citations

An efficient illumination normalization method for face recognition

Xudong Xie;Kin-Man Lam.
Pattern Recognition Letters (2006)

215 Citations

Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection

Hongmei Song;Wenguan Wang;Sanyuan Zhao;Jianbing Shen.
european conference on computer vision (2018)

214 Citations

Face recognition under varying illumination based on a 2D face shape model

Xudong Xie;Kin Man Lam.
Pattern Recognition (2005)

204 Citations

Extraction of the Euclidean skeleton based on a connectivity criterion

Wai Pak Choi;Kin Man Lam;Wan Chi Siu.
Pattern Recognition (2003)

192 Citations

Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image

Xudong Xie;Kin-Man Lam.
IEEE Transactions on Image Processing (2006)

178 Citations

Optimal sampling of Gabor features for face recognition

Dang-Hui Liu;Kin-Man Lam;Lan-Sun Shen.
Pattern Recognition Letters (2004)

167 Citations

Best Scientists Citing Kin-Man Lam

Hong Yan

Hong Yan

City University of Hong Kong

Publications: 28

Jianbing Shen

Jianbing Shen

Beijing Institute of Technology

Publications: 24

Xiang Bai

Xiang Bai

Huazhong University of Science and Technology

Publications: 21

Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

Publications: 19

Wen Gao

Wen Gao

Peking University

Publications: 19

Shiguang Shan

Shiguang Shan

Chinese Academy of Sciences

Publications: 18

Junjun Jiang

Junjun Jiang

Harbin Institute of Technology

Publications: 18

Wenguan Wang

Wenguan Wang

ETH Zurich

Publications: 16

Xilin Chen

Xilin Chen

Institute Of Computing Technology

Publications: 14

Fatih Porikli

Fatih Porikli

Australian National University

Publications: 14

Pong C. Yuen

Pong C. Yuen

Hong Kong Baptist University

Publications: 13

Longin Jan Latecki

Longin Jan Latecki

Temple University

Publications: 13

Witold Pedrycz

Witold Pedrycz

University of Alberta

Publications: 12

Yilong Yin

Yilong Yin

Shandong University

Publications: 12

Jan Flusser

Jan Flusser

Czech Academy of Sciences

Publications: 11

Karim Faez

Karim Faez

Amirkabir University of Technology

Publications: 11

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

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