Similarity quantification of bare footprint based on linear measurement and shape context contour combined score-based likelihood ratio evaluation.

Bare footprints are a type of physical evidence that can be crucial for solving difficult cases. To assist with identification, we explored a similarity quantification method using 3062 bare footprints from 1391 volunteers. We used linear similarity to cover seven linear lengths and then calculated the contour similarity of the heel region, anterior margin, and toe region using the shape context. Discriminant analysis was applied to determine the weights of the linear and contour similarities. Linear similarity had a weight of 0.56 whereas contour similarity had a weight of 0.44. The similarity between the same source and non-matches bare footprints was significantly different, with a leave-one-out cross-validation accuracy of 98.8%. Using the constructed similarity model, we developed a score-based likelihood ratio model based on similarity scores. We applied this model to five representative test samples including different volunteers, six months apart bare footprints, dynamic walking and static bare footprints. This method eliminated the interference of motion states and allowed for accurate determination of the same source and non-matches test samples. Overall, we quantified the shape contour and established a similarity assessment system for bare footprints that can assist in evaluation and identification.