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.
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