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Bridging Domain Gaps for Cross-Spectrum and Long-range Face Recognition Using Domain Adaptive Machine Learning

Cedric A Nimpa Fondje, University of Nebraska - Lincoln


Face recognition technology has witnessed significant advancements in recent decades,enabling its widespread adoption in various applications such as security, surveillance,and biometrics applications. However, one of the primary challenges faced by existingface recognition systems is their limited performance when presented with images fromdifferent modalities or domains( such as infrared to visible, long range to close range,nighttime to daytime, profile to f rontal, e tc.) Additionally, advancements in camerasensors, analytics beyond the visible spectrum, and the increasing size of cross-modaldatasets have led to a particular interest in cross-modal learning for face recognition inthe biometrics and computer vision community. Despite a relatively large gap betweensource and target domains, existing approaches reduce or bridge such domain gapsby either synthesizing face imagery in the target domain using face imagery fromthe source domain, or by learning cross-modal image representations that are robustto both the source and the target domain. Therefore, this dissertation presents thedesign and implementation of a novel domain adaptation framework leveraging robustimage representations to achieve state-of-the art performance in cross-spectrum andlong-range face recognition. The proposed methods use machine learning and deeplearning techniques to (1) efficiently ex tract an d le arn do main-invariant embeddingfrom face imagery, (2) learn a mapping from the source to the target domain, and (3)evaluate the proposed framework on several cross-modal face datasets.

Subject Area

Artificial intelligence|Computer Engineering|Electrical engineering

Recommended Citation

Nimpa Fondje, Cedric A, "Bridging Domain Gaps for Cross-Spectrum and Long-range Face Recognition Using Domain Adaptive Machine Learning" (2023). ETD collection for University of Nebraska-Lincoln. AAI30813692.