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, Pattern recognition, Face, Facial recognition system and Computer vision.
Many of his studies involve connections with topics such as Machine learning and Artificial intelligence.
In his research, Cluster analysis, Algorithm design and Representation is intimately related to Matrix decomposition, which falls under the overarching field of Pattern recognition.
The various areas that Stefanos Zafeiriou examines in his Face study include Texture, Image and Information retrieval.
He combines subjects such as Margin, Pattern recognition, Contextual image classification and Biometrics with his study of Facial recognition system.
His Computer vision research includes elements of Facial expression, Robustness and Benchmark.
His most cited work include:
- ArcFace: Additive Angular Margin Loss for Deep Face Recognition (1181 citations)
- 300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge (685 citations)
- Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network (451 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Computer vision, Face and Facial recognition system are his primary areas of study.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Machine learning.
His study ties his expertise on Subspace topology together with the subject of Pattern recognition.
His work in Computer vision addresses subjects such as Robustness, which are connected to disciplines such as Outlier and Sparse matrix.
He interconnects Object, Texture, Discriminative model and Code in the investigation of issues within Face.
His study in Linear discriminant analysis is interdisciplinary in nature, drawing from both Kernel Fisher discriminant analysis and Dimensionality reduction.
He most often published in these fields:
- Artificial intelligence (80.32%)
- Pattern recognition (36.93%)
- Computer vision (32.61%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (80.32%)
- Computer vision (32.61%)
- Face (25.61%)
In recent papers he was focusing on the following fields of study:
Stefanos Zafeiriou mainly focuses on Artificial intelligence, Computer vision, Face, Pattern recognition and Facial expression.
His research on Artificial intelligence frequently connects to adjacent areas such as Machine learning.
He focuses mostly in the field of Computer vision, narrowing it down to topics relating to Head and, in certain cases, Adaptation.
The concepts of his Face study are interwoven with issues in Texture, Translation and Pattern recognition.
His biological study spans a wide range of topics, including Focus, Boosting and Vertex.
His Facial recognition system research is multidisciplinary, incorporating elements of Data science and Gesture recognition.
Between 2019 and 2021, his most popular works were:
- RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild (52 citations)
- Analysing Affective Behavior in the First ABAW 2020 Competition. (46 citations)
- Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Pattern recognition, Face, Convolutional neural network and Computer vision.
Artificial intelligence is represented through his Artificial neural network, Facial recognition system, Facial expression, Representation and Image research.
His Facial recognition system study combines topics from a wide range of disciplines, such as Gaze and Task.
As a part of the same scientific family, Stefanos Zafeiriou mostly works in the field of Pattern recognition, focusing on Boosting and, on occasion, Component and Ranking.
His Face research includes themes of Texture, Translation, Data science and Pattern recognition.
His Convolutional neural network research is multidisciplinary, incorporating perspectives in Motion, Normalization and Discriminative learning.
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