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 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.
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.
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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Locating and extracting the eye in human face images
Kin-Man Lam;Hong Yan.
Pattern Recognition (1996)
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)
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)
Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection
Hongmei Song;Wenguan Wang;Sanyuan Zhao;Jianbing Shen.
european conference on computer vision (2018)
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)
An efficient illumination normalization method for face recognition
Xudong Xie;Kin-Man Lam.
Pattern Recognition Letters (2006)
Face recognition under varying illumination based on a 2D face shape model
Xudong Xie;Kin Man Lam.
Pattern Recognition (2005)
Extraction of the Euclidean skeleton based on a connectivity criterion
Wai Pak Choi;Kin Man Lam;Wan Chi Siu.
Pattern Recognition (2003)
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)
Optimal sampling of Gabor features for face recognition
Dang-Hui Liu;Kin-Man Lam;Lan-Sun Shen.
Pattern Recognition Letters (2004)
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