What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Facial recognition system, Pattern recognition, Computer vision and Face.
His Artificial intelligence study frequently draws parallels with other fields, such as Machine learning.
His Facial recognition system study combines topics in areas such as Image processing, Gabor filter, Facial expression and Subspace topology.
His work on Linear discriminant analysis as part of general Pattern recognition research is often related to Gabor wavelet, thus linking different fields of science.
His work in the fields of Computer vision, such as Local binary patterns, Histogram and Preprocessor, intersects with other areas such as Set.
Shiguang Shan has researched Face in several fields, including Text mining, Pose, Similarity and Database.
His most cited work include:
- Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition (911 citations)
- WLD: A Robust Local Image Descriptor (784 citations)
- The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations (772 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, Facial recognition system, Computer vision and Face.
His study looks at the relationship between Artificial intelligence and fields such as Machine learning, as well as how they intersect with chemical problems.
His Pattern recognition research is multidisciplinary, incorporating elements of Contextual image classification, Histogram and Robustness.
His study in Facial recognition system is interdisciplinary in nature, drawing from both Image processing, Subspace topology, Facial expression and Biometrics.
Computer vision is closely attributed to AdaBoost in his research.
His work carried out in the field of Face brings together such families of science as Speech recognition and Representation.
He most often published in these fields:
- Artificial intelligence (95.22%)
- Pattern recognition (55.65%)
- Facial recognition system (41.30%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (95.22%)
- Pattern recognition (55.65%)
- Face (30.22%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Face, Computer vision and Discriminative model.
His work on Feature extraction, Facial recognition system and Feature as part of general Artificial intelligence study is frequently connected to Task analysis, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
He has included themes like Training set and Robustness in his Facial recognition system study.
His work on Feature learning as part of general Pattern recognition research is frequently linked to Set, bridging the gap between disciplines.
His biological study spans a wide range of topics, including End-to-end principle, Generator and Expression.
His work on Image as part of general Computer vision research is frequently linked to Frame, thereby connecting diverse disciplines of science.
Between 2018 and 2021, his most popular works were:
- AttGAN: Facial Attribute Editing by Only Changing What You Want (224 citations)
- Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism (141 citations)
- Interaction-And-Aggregation Network for Person Re-Identification (81 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Shiguang Shan mainly focuses on Artificial intelligence, Pattern recognition, Face, Facial recognition system and Feature extraction.
His Artificial intelligence study frequently draws connections between adjacent fields such as Computer vision.
His work in the fields of Segmentation overlaps with other areas such as Set.
His Face research includes elements of Representation and Image.
His Facial recognition system study integrates concerns from other disciplines, such as Normalization, Sketch recognition and Invariant.
His research investigates the connection between Feature extraction and topics such as Scene graph that intersect with issues in Intersection, Cognitive neuroscience of visual object recognition and Visual reasoning.
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