What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Geometry
Artificial intelligence, Computer vision, Facial recognition system, Pattern recognition and Cognitive neuroscience of visual object recognition are his primary areas of study.
As part of one scientific family, he deals mainly with the area of Artificial intelligence, narrowing it down to issues related to the Linear subspace, and often Subspace topology.
His studies link Function with Computer vision.
His research investigates the connection between Facial recognition system and topics such as Manifold that intersect with issues in Representation.
His work carried out in the field of Pattern recognition brings together such families of science as Facial motion capture, Eigenface, Three-dimensional face recognition and Expression.
His research integrates issues of Lambertian reflectance, Face detection, Pose, Surface of revolution and Orthographic projection in his study of Cognitive neuroscience of visual object recognition.
His most cited work include:
- Eigenfaces vs. Fisherfaces: recognition using class specific linear projection (10464 citations)
- From few to many: illumination cone models for face recognition under variable lighting and pose (4110 citations)
- Detecting faces in images: a survey (3268 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Facial recognition system and Iterative reconstruction.
The study of Artificial intelligence is intertwined with the study of Surface in a number of ways.
His Computer vision study combines topics from a wide range of disciplines, such as Reflectivity and Mobile robot.
As part of his studies on Pattern recognition, David J. Kriegman often connects relevant subjects like Feature.
He usually deals with Facial recognition system and limits it to topics linked to Linear subspace and Subspace topology.
His studies in Cognitive neuroscience of visual object recognition integrate themes in fields like Lambertian reflectance, Feature extraction, Pose, Invariant and Real image.
He most often published in these fields:
- Artificial intelligence (76.13%)
- Computer vision (60.36%)
- Pattern recognition (14.86%)
What were the highlights of his more recent work (between 2012-2020)?
- Artificial intelligence (76.13%)
- Computer vision (60.36%)
- Pattern recognition (14.86%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Artificial neural network.
His Computer vision research is multidisciplinary, incorporating perspectives in Deconvolution and Scattering.
In his research on the topic of Pattern recognition, Image is strongly related with Medical imaging.
David J. Kriegman interconnects Sequence and Key frame in the investigation of issues within Machine learning.
The concepts of his Artificial neural network study are interwoven with issues in Margin, Scale, Boundary detection and Feature learning.
His Facial recognition system research includes themes of Camera auto-calibration, Camera resectioning, Perspective and Pose.
Between 2012 and 2020, his most popular works were:
- Localizing Parts of Faces Using a Consensus of Exemplars (352 citations)
- Image to Image Translation for Domain Adaptation (275 citations)
- Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation. (116 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Optics
David J. Kriegman spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, MNIST database and Machine learning.
Object is the focus of his Artificial intelligence research.
David J. Kriegman has researched Pattern recognition in several fields, including Artificial neural network, Boosting and Image.
He combines subjects such as Scattering, Optics and Multiangle light scattering with his study of Computer vision.
The concepts of his MNIST database study are interwoven with issues in Segmentation and Image translation.
His Feature extraction research incorporates themes from Biometrics, Expression, Facial recognition system, Face and Shape analysis.
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