Extraction of shoe-print patterns from impression evidence using Conditional Random Fields

Impression evidence in the form of shoe-prints are commonly found in crime scenes. A critical step in automatic shoe-print identification is extraction of the shoe-print pattern. It involves isolating the shoe-print foreground (impressions made by the shoe) from the remaining elements (background and noise). The problem is formulated as one of labeling the regions of a shoeprint image as foreground/background. It is formulated as a machine learning task which is approached using a probabilistic model, i.e., conditional random fields (CRFs). Since the model exploits the inherent long range dependencies that exist in the shoe-print it is more robust than other approaches, e.g., neural networks and adaptive thresholding of grey-scale images into binary. This was demonstrated using a data set of 45 shoeprint image pairs representing latent and known shoe-print images.