What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Kar-Ann Toh mainly investigates Artificial intelligence, Pattern recognition, Biometrics, Facial recognition system and Computer vision.
His Artificial intelligence research incorporates themes from Machine learning and Data mining.
In his research on the topic of Data mining, Pattern recognition, Decision tree learning and Decision tree is strongly related with Artificial neural network.
The Pattern recognition study combines topics in areas such as Curse of dimensionality, Skin color, Boosting, Local binary patterns and Color normalization.
Kar-Ann Toh usually deals with Facial recognition system and limits it to topics linked to Word error rate and Nonlinear system, Models of neural computation, Gradient descent and Random projection.
In general Computer vision, his work in Fingerprint recognition, Fingerprint and Three-dimensional face recognition is often linked to Positioning aids linking many areas of study.
His most cited work include:
- Extreme Learning Machine (558 citations)
- Alignment-Free Cancelable Fingerprint Templates Based on Local Minutiae Information (124 citations)
- Benchmarking a reduced multivariate polynomial pattern classifier (118 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Biometrics and Facial recognition system.
Kar-Ann Toh focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Machine learning and, in certain cases, Data mining.
His Pattern recognition research is multidisciplinary, incorporating elements of Image, Sensor fusion and Word error rate.
In general Computer vision study, his work on Image fusion, Pixel, Projection and Face detection often relates to the realm of Identity, thereby connecting several areas of interest.
His Biometrics study also includes
- Algorithm and related Minification,
- Fingerprint which connect with Fingerprint recognition and Minutiae.
His research in Facial recognition system intersects with topics in Feature, Robustness, Kernel method and Random projection.
He most often published in these fields:
- Artificial intelligence (75.69%)
- Pattern recognition (45.30%)
- Computer vision (25.97%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (75.69%)
- Pattern recognition (45.30%)
- Algorithm (16.02%)
In recent papers he was focusing on the following fields of study:
Kar-Ann Toh mostly deals with Artificial intelligence, Pattern recognition, Algorithm, Feature extraction and Kernel.
Artificial intelligence is represented through his Projection, Feature, Principal component analysis, Facial recognition system and Face research.
His studies deal with areas such as RGB color model, Subspace topology and Image as well as Pattern recognition.
Kar-Ann Toh has included themes like Moore–Penrose pseudoinverse and Radial basis function in his Algorithm study.
The study incorporates disciplines such as Extreme learning machine, Normalization and Kernel in addition to Feature extraction.
His Extreme learning machine research incorporates elements of Visualization and Minification.
Between 2017 and 2021, his most popular works were:
- Micro-Doppler Mini-UAV Classification Using Empirical-Mode Decomposition Features (38 citations)
- An Analytic Gabor Feedforward Network for Single-Sample and Pose-Invariant Face Recognition (15 citations)
- Biomedical image classification based on a cascade of an SVM with a reject option and subspace analysis (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Feature extraction, Image segmentation and Line.
Local binary patterns and RGB color model are the primary areas of interest in his Artificial intelligence study.
His Pattern recognition study incorporates themes from Similarity, Feed forward, Facial recognition system, Frequency domain and Invariant.
His Feature extraction research includes elements of Normalization, Extreme learning machine, Matrix multiplication, Discriminative model and Kernel.
His Image segmentation study combines topics from a wide range of disciplines, such as Intelligent decision support system, Computer architecture and Manual annotation.
His research investigates the connection between Algorithm and topics such as Support vector machine that intersect with problems in Subspace topology, Scale-invariant feature transform, Noise and Image.
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