Author ORCID Identifier
Semester
Fall
Date of Graduation
2023
Document Type
Dissertation
Degree Type
PhD
College
Statler College of Engineering and Mineral Resources
Department
Lane Department of Computer Science and Electrical Engineering
Committee Chair
Nasser Nasrabadi
Committee Member
Brian Powell
Committee Member
Elaine Eschen
Committee Member
Jeremy Dawson
Committee Member
Matthew Valenti
Committee Member
Shuowen Hu
Abstract
The over-arching theme of this dissertation is the development of automated detection and/or classification systems for challenging infrared scenarios. The six works presented herein can be categorized into four problem scenarios. In the first scenario, long-distance detection and classification of vehicles in thermal imagery, a custom convolutional network architecture is proposed for small thermal target detection. For the second scenario, thermal face landmark detection and thermal cross-spectral face verification, a publicly-available visible and thermal face dataset is introduced, along with benchmark results for several landmark detection and face verification algorithms. Furthermore, a novel visible-to-thermal transfer learning algorithm for face landmark detection is presented. The third scenario addresses near-infrared cross-spectral periocular recognition with a coupled conditional generative adversarial network guided by auxiliary synthetic loss functions. Finally, a deep sparse feature selection and fusion is proposed to detect the presence of textured contact lenses prior to near-infrared iris recognition.
Recommended Citation
Poster, Domenick D., "Object Detection and Classification in the Visible and Infrared Spectrums" (2023). Graduate Theses, Dissertations, and Problem Reports. 12179.
https://researchrepository.wvu.edu/etd/12179