What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Artificial intelligence, Computer vision, Pattern recognition, Facial recognition system and Biometrics are his primary areas of study.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Machine learning.
As a part of the same scientific family, he mostly works in the field of Computer vision, focusing on Detector and, on occasion, Edge detection, Enhanced Data Rates for GSM Evolution, Edge and Edge detector.
The various areas that he examines in his Pattern recognition study include Artificial neural network, Algorithm and Receiver operating characteristic.
Kevin W. Bowyer interconnects Classifier, Prior probability and Naive Bayes classifier in the investigation of issues within Receiver operating characteristic.
His Biometrics study combines topics in areas such as Hamming distance and Word error rate.
His most cited work include:
- SMOTE: synthetic minority over-sampling technique (9316 citations)
- SMOTE: Synthetic Minority Over-sampling Technique (4150 citations)
- Overview of the face recognition grand challenge (2084 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Biometrics and Facial recognition system.
His Machine learning research extends to the thematically linked field of Artificial intelligence.
Training set is closely connected to Data mining in his research, which is encompassed under the umbrella topic of Machine learning.
The various areas that Kevin W. Bowyer examines in his Pattern recognition study include Image, Feature and Data set.
His Biometrics research is multidisciplinary, incorporating perspectives in Speech recognition, Robustness and Pattern recognition.
His work carried out in the field of Facial recognition system brings together such families of science as Statistics, Principal component analysis and Facial expression.
He most often published in these fields:
- Artificial intelligence (73.54%)
- Computer vision (40.42%)
- Pattern recognition (30.62%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (73.54%)
- Iris recognition (21.87%)
- Pattern recognition (30.62%)
In recent papers he was focusing on the following fields of study:
Kevin W. Bowyer mainly investigates Artificial intelligence, Iris recognition, Pattern recognition, Facial recognition system and Biometrics.
He frequently studies issues relating to Computer vision and Artificial intelligence.
His Iris recognition research includes themes of Presentation, State, IRIS, Feature extraction and Iris.
His Pattern recognition study incorporates themes from Domain and Feature.
His Facial recognition system study also includes
- Statistics that intertwine with fields like Training set,
- Facial expression together with Similarity.
His Biometrics study combines topics from a wide range of disciplines, such as Quality and Liveness.
Between 2017 and 2021, his most popular works were:
- Presentation Attack Detection for Iris Recognition: An Assessment of the State-of-the-Art (57 citations)
- Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection (26 citations)
- Characterizing the Variability in Face Recognition Accuracy Relative to Race (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His primary scientific interests are in Artificial intelligence, Iris recognition, Facial recognition system, Statistics and Face.
Within one scientific family, Kevin W. Bowyer focuses on topics pertaining to Computer vision under Artificial intelligence, and may sometimes address concerns connected to Hallucinating.
His Iris recognition research is multidisciplinary, relying on both Presentation, Liveness, Iris and Pattern recognition.
Kevin W. Bowyer focuses mostly in the field of Pattern recognition, narrowing it down to matters related to Feature and, in some cases, Domain, Aggregate and Discriminative model.
As a part of the same scientific study, Kevin W. Bowyer usually deals with the Statistics, concentrating on Training set and frequently concerns with Facial expression and Similarity.
His Face research integrates issues from Contrast, Deep cnn and Identification.
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