Domain Adaptation for Enhancing Visual Hand Landmark Prediction AI in Infrared Imaging
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Abstract
In this
work, we investigate how domain adaptation techniques can improve the
performance of hand landmark detection models originally trained on RGB images
when deployed on infrared (IR) data. Our motivation stems from a medical use
case in Nepal, where clinicians require reliable temperature estimation at hand
keypoints to detect early signs of leprosy. We evaluate three methods on a small
IR dataset (80 labeled images & 5000 unlabeled frames): a shallow
adaptation (AdaBN), a deep alignment approach (Deep CORAL), and a test-time
subspace alignment method (SSA). Our experiments show that while AdaBN and SSA
yield moderate improvements, Deep CORAL achieves stronger gains through
targeted training of specific model components. The combination of these
methods produces superior results, yielding an 11% improvement in percentage of
correct keypoints (PCK@0.05) on our custom annotated IR dataset. These findings
demonstrate that combining lightweight and deep domain adaptation approaches
can effectively enhance IR hand landmark detection accuracy without requiring
large labeled datasets, enabling practical deployment for clinical thermal
imaging in resource-limited settings.