A novel approach for latent print identification using accurate overlays to prioritize reference prints

A novel approach to automated fingerprint matching and scoring that produces accurate locally and nonlinearly adjusted overlays of a latent print onto each reference print in a corpus is described. The technology, which addresses challenges inherent to latent prints, provides the latent print examiner with a prioritized ranking of candidate reference prints based on the overlays of the latent onto each candidate print. In addition to supporting current latent print comparison practices, this approach can make it possible to return a greater number of AFIS candidate prints because the ranked overlays provide a substantial starting point for latent-to-reference print comparison. To provide the image information required to create an accurate overlay of a latent print onto a reference print, "Ridge-Specific Markers" (RSMs), which correspond to short continuous segments of a ridge or furrow, are introduced. RSMs are reliably associated with any specific local section of a ridge or a furrow using the geometric information available from the image. Latent prints are commonly fragmentary, with reduced clarity and limited minutiae (i.e., ridge endings and bifurcations). Even in the absence of traditional minutiae, latent prints contain very important information in their ridges that permit automated matching using RSMs. No print orientation or information beyond the RSMs is required to generate the overlays. This automated process is applied to the 88 good quality latent prints in the NIST Special Database (SD) 27. Nonlinear overlays of each latent were produced onto all of the 88 reference prints in the NIST SD27. With fully automated processing, the true mate reference prints were ranked in the first candidate position for 80.7% of the latents tested, and 89.8% of the true mate reference prints ranked in the top ten positions. After manual post-processing of those latents for which the true mate reference print was not ranked first, these frequencies increased to 90.9% (1st rank) and 96.6% (top ten), respectively. Because the computational process is highly parallelizable, it is feasible for this method to work with a reference corpus of several thousand prints.