J
Jeremy Dawson
Researcher at West Virginia University
Publications - 133
Citations - 1316
Jeremy Dawson is an academic researcher from West Virginia University. The author has contributed to research in topics: Convolutional neural network & Facial recognition system. The author has an hindex of 16, co-authored 126 publications receiving 920 citations. Previous affiliations of Jeremy Dawson include University College of Engineering.
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Journal ArticleDOI
3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition
TL;DR: This paper proposes the use of a coupled 3D convolutional neural network (3D CNN) architecture that can map both modalities into a representation space to evaluate the correspondence of audio–visual streams using the learned multimodal features.
Journal ArticleDOI
Nanotopographical Modulation of Cell Function through Nuclear Deformation.
Kai Wang,Allison Bruce,Ryan Mezan,Anand Kadiyala,Liying Wang,Jeremy Dawson,Yon Rojanasakul,Yong Yang +7 more
TL;DR: This study delineated the relationships between focal adhesions, nucleus and cell function and highlighted that the nanotopography could regulate cell phenotype and function by modulating nuclear deformation, indicating that the nucleus serves as a critical mechanosensor for cell regulation.
Proceedings ArticleDOI
Fast Geometrically-Perturbed Adversarial Faces
TL;DR: A fast landmark manipulation method for generating adversarial faces is proposed, which is approximately 200 times faster than the previous geometric attacks and obtains 99.86% success rate on the state-of-the-art face recognition models.
Proceedings ArticleDOI
Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification
TL;DR: In this paper, a deep multimodal fusion network is proposed to fuse multiple modalities (face, iris, and fingerprint) for person identification, which consists of multiple streams of modality-specific CNNs, which are jointly optimized at multiple feature abstraction levels.
Journal ArticleDOI
Hyperspectral Imaging and K-Means Classification for Histologic Evaluation of Ductal Carcinoma In Situ.
TL;DR: In the hyperspectral image analysis, the image processing algorithm, K-means, shows the greatest potential for building a semi-automated system that could identify and sort between normal and ductal carcinoma in situ tissues.