2019 - Fellow of the American Association for the Advancement of Science (AAAS)
2014 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to computer vision, pattern recognition and biometrics
2014 - Edward J. McCluskey Technical Achievement Award, IEEE Computer Society For pioneering contributions to the science and engineering of biometrics
1998 - IEEE Fellow For contributions to algorithms for recognizing objects in images.
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 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.
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
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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SMOTE: synthetic minority over-sampling technique
Nitesh V. Chawla;Kevin W. Bowyer;Lawrence O. Hall;W. Philip Kegelmeyer.
Journal of Artificial Intelligence Research (2002)
SMOTE: Synthetic Minority Over-sampling Technique
N. V. Chawla;K. W. Bowyer;L. O. Hall;W. P. Kegelmeyer.
arXiv: Artificial Intelligence (2011)
Overview of the face recognition grand challenge
P.J. Phillips;P.J. Flynn;T. Scruggs;K.W. Bowyer.
computer vision and pattern recognition (2005)
Current Status of the Digital Database for Screening Mammography
Michael D. Heath;Kevin W. Bowyer;Daniel B. Kopans;W. Philip Kegelmeyer.
Digital Mammography / IWDM (1998)
SMOTEBoost: Improving Prediction of the Minority Class in Boosting
Nitesh V. Chawla;Aleksandar Lazarevic;Lawrence O. Hall;Kevin W. Bowyer.
european conference on principles of data mining and knowledge discovery (2003)
The humanID gait challenge problem: data sets, performance, and analysis
S. Sarkar;P.J. Phillips;Z. Liu;I.R. Vega.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)
Combination of multiple classifiers using local accuracy estimates
K. Woods;W.P. Kegelmeyer;K. Bowyer.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1997)
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Kevin W. Bowyer;Kyong Chang;Patrick Flynn.
Computer Vision and Image Understanding (2006)
An experimental comparison of range image segmentation algorithms
A. Hoover;G. Jean-Baptiste;X. Jiang;P.J. Flynn.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1996)
Image understanding for iris biometrics: A survey
Kevin W. Bowyer;Karen Hollingsworth;Patrick J. Flynn.
Computer Vision and Image Understanding (2008)
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