What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
Gerard Medioni mostly deals with Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Facial recognition system.
Face, Edge detection, Object detection, Feature extraction and Segmentation are among the areas of Artificial intelligence where Gerard Medioni concentrates his study.
The study incorporates disciplines such as Computer graphics and Robustness in addition to Computer vision.
His Algorithm research incorporates elements of Orientation, Curvature, Topology and Theoretical computer science.
His Pattern recognition research is multidisciplinary, incorporating elements of Outlier, Motion capture and Gesture recognition.
In the field of Facial recognition system, his study on Three-dimensional face recognition overlaps with subjects such as Identity.
His most cited work include:
- Object modelling by registration of multiple range images (2193 citations)
- Object modeling by registration of multiple range images (1155 citations)
- Structural indexing: efficient 3-D object recognition (477 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Face are his primary areas of study.
His Image segmentation, Facial recognition system, Segmentation, Robustness and Feature extraction study are his primary interests in Artificial intelligence.
His research combines Convolutional neural network and Facial recognition system.
Tracking, Object, Motion estimation, Image processing and Epipolar geometry are among the areas of Computer vision where the researcher is concentrating his efforts.
He studies Discriminative model, a branch of Pattern recognition.
His study brings together the fields of Tensor and Algorithm.
He most often published in these fields:
- Artificial intelligence (84.88%)
- Computer vision (69.30%)
- Pattern recognition (20.99%)
What were the highlights of his more recent work (between 2012-2020)?
- Artificial intelligence (84.88%)
- Computer vision (69.30%)
- Pattern recognition (20.99%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Face and Facial recognition system.
His Artificial intelligence study is mostly concerned with Convolutional neural network, Robustness, Three-dimensional face recognition, Landmark and Feature extraction.
His work in Computer vision addresses issues such as Zoom, which are connected to fields such as Graph traversal.
His Pattern recognition study also includes fields such as
- Facial expression that connect with fields like Solid modeling,
- Deep learning, which have a strong connection to Emotion recognition.
His study in the field of Facial motion capture is also linked to topics like Set.
His Facial recognition system study combines topics in areas such as Matching, Image, Leverage and Identification.
Between 2012 and 2020, his most popular works were:
- Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network (287 citations)
- Pose-Aware Face Recognition in the Wild (201 citations)
- Do We Really Need to Collect Millions of Faces for Effective Face Recognition (191 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Facial recognition system and Convolutional neural network.
His work is connected to Face, Three-dimensional face recognition, Landmark, Discriminative model and Feature extraction, as a part of Artificial intelligence.
His Computer vision study combines topics from a wide range of disciplines, such as Motion and Robustness.
His biological study spans a wide range of topics, including Deep learning, Facial expression and Motion.
Gerard Medioni usually deals with Facial recognition system and limits it to topics linked to Leverage and Image quality, Pooling, Template matching and Template based.
His Object research integrates issues from Pose, Position and Anticipation.
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.
