What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Computer vision, Artificial intelligence, Digital camera, Facial expression and Channel are his primary areas of study. His Face, Motion and Motion estimation study in the realm of Computer vision interacts with subjects such as Reflection. His studies deal with areas such as Machine vision and Pattern recognition as well as Face.
Artificial intelligence and Mobile device are commonly linked in his work. The concepts of his Facial expression study are interwoven with issues in Online video, Cognitive psychology, Social psychology, Natural language processing and Facial recognition system. His research integrates issues of Cyan, Pixel, RGB color model and Region of interest in his study of Channel.
His most cited work include:
- Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. (1026 citations)
- Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam (857 citations)
- Method and system for measurement of physiological parameters (189 citations)
What are the main themes of his work throughout his whole career to date?
Daniel McDuff mainly focuses on Artificial intelligence, Computer vision, Facial expression, Human–computer interaction and Cognitive psychology. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning, Pattern recognition and Natural language processing. Daniel McDuff undertakes multidisciplinary investigations into Computer vision and Photoplethysmogram in his work.
His work deals with themes such as Crowdsourcing, Valence, Speech recognition and Facial recognition system, which intersect with Facial expression. The various areas that he examines in his Human–computer interaction study include Embodied cognition, Dialog system, Gesture and Set. Daniel McDuff has included themes like Affect and Reinforcement learning in his Cognitive psychology study.
He most often published in these fields:
- Artificial intelligence (39.77%)
- Computer vision (21.05%)
- Facial expression (19.30%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (39.77%)
- Benchmark (4.68%)
- Machine learning (9.94%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Benchmark, Machine learning, Facial expression and Human–computer interaction. His studies in Artificial intelligence integrate themes in fields like Natural language processing, Computer vision and Pattern recognition. Daniel McDuff works in the field of Computer vision, focusing on Data compression in particular.
His Machine learning research includes elements of Emoji and Selection. His Facial expression study combines topics in areas such as Valence, Dialog system, Face and Embodied cognition. His biological study spans a wide range of topics, including Control, Affect and Gesture.
Between 2019 and 2021, his most popular works were:
- Non-contact imaging of peripheral hemodynamics during cognitive and psychological stressors (9 citations)
- DeepMag: Source-Specific Change Magnification Using Gradient Ascent (6 citations)
- Optimizing for Happiness and Productivity: Modeling Opportune Moments for Transitions and Breaks at Work (5 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His primary scientific interests are in Artificial intelligence, Photoplethysmogram, Videoconferencing, Biomedical engineering and Sample size determination. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Set. Photoplethysmogram and Pulse are frequently intertwined in his study.
He interconnects Public speaking, Knowledge management and Turn-taking in the investigation of issues within Videoconferencing. His Audio visual research is multidisciplinary, incorporating perspectives in Active learning and Feature learning. His work carried out in the field of Deep learning brings together such families of science as Gradient descent, Motion, Feature extraction and Magnification.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
