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, Pattern recognition, Computer vision, Feature extraction and Biometrics.
His study in Machine learning extends to Artificial intelligence with its themes.
He focuses mostly in the field of Pattern recognition, narrowing it down to matters related to Facial recognition system and, in some cases, Word error rate.
His study in the field of Image segmentation, Image processing and Face is also linked to topics like Gait analysis.
His Feature extraction study integrates concerns from other disciplines, such as Pixel, Feature vector, Contextual image classification, Discriminative model and Convolutional neural network.
In his study, Handwriting is strongly linked to Identification, which falls under the umbrella field of Biometrics.
His most cited work include:
- A survey on visual surveillance of object motion and behaviors (1920 citations)
- Recent developments in human motion analysis (924 citations)
- Silhouette analysis-based gait recognition for human identification (920 citations)
What are the main themes of his work throughout his whole career to date?
Tieniu Tan spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Biometrics.
The study of Artificial intelligence is intertwined with the study of Machine learning in a number of ways.
His Pattern recognition research incorporates themes from Contextual image classification, Histogram and Feature.
The various areas that he examines in his Feature extraction study include Classifier and Edge detection.
Biometrics is closely attributed to Identification in his study.
His Iris recognition research integrates issues from IRIS and Iris.
He most often published in these fields:
- Artificial intelligence (88.27%)
- Pattern recognition (50.42%)
- Computer vision (46.93%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (88.27%)
- Pattern recognition (50.42%)
- Computer vision (46.93%)
In recent papers he was focusing on the following fields of study:
Tieniu Tan focuses on Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Image.
His works in Deep learning, Convolutional neural network, Discriminative model, Feature extraction and Pixel are all subjects of inquiry into Artificial intelligence.
Tieniu Tan interconnects Ranking, Data mining and Pattern recognition in the investigation of issues within Deep learning.
His Pattern recognition research incorporates elements of Margin, Facial recognition system, Invariant and Feature.
His Computer vision research includes elements of Normal and Robustness.
His work carried out in the field of Image brings together such families of science as Natural language processing, Key, Embedding and Benchmark.
Between 2016 and 2021, his most popular works were:
- A Light CNN for Deep Face Representation With Noisy Labels (505 citations)
- A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs (256 citations)
- Session-Based Recommendation with Graph Neural Networks (217 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Facial recognition system.
His study on Class is often connected to Action recognition as part of broader study in Pattern recognition.
His Biometrics, Iris recognition and Pose study in the realm of Computer vision interacts with subjects such as Lenslet.
Tieniu Tan has researched Biometrics in several fields, including Field and Fingerprint.
His Machine learning research is multidisciplinary, incorporating perspectives in Embedding, Feature extraction and Social media.
His studies deal with areas such as Classifier, Cluster analysis and Feature vector as well as Feature extraction.
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