What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algebra
His primary scientific interests are in Artificial intelligence, Machine learning, Biometrics, Support vector machine and Pattern recognition.
His study in Machine learning is interdisciplinary in nature, drawing from both Facial recognition system and Cognitive neuroscience of visual object recognition.
His Facial recognition system study incorporates themes from Generalization and 3D single-object recognition.
His Biometrics study combines topics from a wide range of disciplines, such as Transformation, Computer network, Identity and Cryptography.
He has researched Support vector machine in several fields, including Visualization, Training set, Psychophysics and Computer vision.
Walter J. Scheirer combines subjects such as Normalization, Image retrieval, Fingerprint recognition and Image fusion with his study of Pattern recognition.
His most cited work include:
- Toward Open Set Recognition (379 citations)
- Cracking Fuzzy Vaults and Biometric Encryption (260 citations)
- Probability Models for Open Set Recognition (241 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Machine learning, Facial recognition system, Pattern recognition and Computer vision.
All of his Artificial intelligence and Deep learning, Face, Convolutional neural network, Biometrics and Artificial neural network investigations are sub-components of the entire Artificial intelligence study.
His Biometrics research is multidisciplinary, incorporating perspectives in Mobile device and Cryptography.
Walter J. Scheirer interconnects Bayesian probability, Cognitive neuroscience of visual object recognition, Visual Psychophysics and Random search in the investigation of issues within Machine learning.
His work carried out in the field of Facial recognition system brings together such families of science as Image processing, Image retrieval and Set.
His Pattern recognition research incorporates elements of Normalization and Face hallucination.
He most often published in these fields:
- Artificial intelligence (61.88%)
- Machine learning (26.24%)
- Facial recognition system (19.31%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (61.88%)
- Machine learning (26.24%)
- Pattern recognition (17.82%)
In recent papers he was focusing on the following fields of study:
Walter J. Scheirer mainly investigates Artificial intelligence, Machine learning, Pattern recognition, Deep learning and Convolutional neural network.
His work on Computer vision expands to the thematically related Artificial intelligence.
In his research on the topic of Machine learning, Mixture model, Multivariate statistics, Inference and Covariate is strongly related with Bayesian probability.
His research on Pattern recognition also deals with topics like
- Face hallucination which is related to area like Identity,
- Generalization and related Ground truth.
His Deep learning research is multidisciplinary, incorporating elements of Robotics, The Internet, Sensory system and Human–computer interaction.
Walter J. Scheirer has included themes like Visual Psychophysics, Machine vision and Human brain in his Convolutional neural network study.
Between 2017 and 2021, his most popular works were:
- The limits and potentials of deep learning for robotics (211 citations)
- The Extreme Value Machine (93 citations)
- Using human brain activity to guide machine learning. (37 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Walter J. Scheirer mostly deals with Artificial intelligence, Machine learning, Convolutional neural network, Facial recognition system and Deep learning.
His Artificial intelligence study integrates concerns from other disciplines, such as Psychophysics and Set.
The various areas that Walter J. Scheirer examines in his Machine learning study include Stimulus, Visual Psychophysics, Random search and Machine translation.
His Facial recognition system research includes elements of Personally identifiable information and Biometrics.
His research in Deep learning intersects with topics in Exploit, Backdoor and Robotics.
His work deals with themes such as Machine vision and Human brain, which intersect with Cognitive neuroscience of visual object recognition.
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.
