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Unconstrained Thermal Hand Segmentation

Robust segmentation of thermal hand images, a trivial task in controlled environments, can be difficult for unconstrained acquisition with a thermal camera, when the temperature of the object differs little from this of a background. This paper implements a method for segmenting hand images collected in the thermal spectrum involving a pre-trained, off-the-shelf data-driven model, fine-tuned with multiple databases of thermal hand images and corresponding, manually annotated ground truth masks. This allows superior performance for difficult samples, compared against conventional methods such as Otsu’s thresholding and Gaussian Mixture Modeling. The segmentation accuracy for good quality samples is comparable with these traditional methods, while at the same time being more unconstrained acquisition-resistant. An improvement of up to 20% in accuracy measured by intersection over union is observed for difficult samples, such as hands that were partially colder than the background, as well as those with wristwatches and jewelry. This robust segmentation in various acquisition scenarios allows not only the correct localization of regions of interest for feature extraction, but also for extracting accurate hand geometry information. Together with the results of experiments, involving multiple training and testing scenarios on three different databases, we provide codes, model weights, and ground truth masks to ensure reproducibility and facilitate further research.