Investigating Bounding Box, Landmark, and Segmentation Approaches for Automatic Human Barefoot Print Classification on Soil Substrates Using Deep Learning

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Abstract
Background/Objectives: This study investigated the use of artificial intelligence (AI) to identify and match barefoot prints belonging to the same individual on soft and sandy soil substrates. Recognizing footprints on soil is challenging due to low contrast and variability in impressions. Methods: We introduce Deep Learning Footprint Identification Technology (DeepFIT), based on a modified You Only Look Once (YOLOv11s) algorithm, using three methods, namely, Bounding Box (BBox), 16 anatomical landmarks, and automatically segmented outlines (Auto-Seg). An Extra Small Detection Head (XSDH) was added to improve feature extraction at smaller scales and enhance generalization through multi-scale supervision, reducing overfitting to specific spatial patterns. Results: Forty adults (20 males, 20 females) participated, with 600 images per individual. As the number of individuals in model training increased, the BBox model’s accuracy declined, resulting in misclassification on the test set. The average performance accuracy across both substrates was 77% for BBox, 90% for segmented outlines, and 96% for anatomical landmarks. Conclusions: The landmark method was the most reliable for identifying and matching barefoot prints on both soft and sandy soils. This approach can assist forensic practitioners in linking suspects to crime scenes and reconstructing events from footprint evidence, providing a valuable tool for forensic investigations.
Funding Information
  • U.S Army Research Office (W911NF-23-1-0293)

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