The Nail to Nail (N2N) Fingerprint Challenge seeks to identify fingerprint collection solutions able to acquire nail-to-nail image capture of the friction ridge surface without the need of a human operator. Collection of N2N, or rolled, fingerprint images allows for improved recognition performance in live and forensic matching scenarios. Traditionally, fingerprints can be grouped into three types: plain (or slap), rolled (or nail-to-nail), and latent (i.e., those found at a crime scene which must be developed through dusting, fuming, or other techniques). Plain prints represent information from only the center portion of the finger pad, whereas rolled prints represent information around the entirety of the finger pad, from one nail edge all the way around to the other nail edge. Latent prints are those left behind on a surface when the person is no longer present. Latent prints are typically partial or degraded in quality, due to the nature of how they are left behind or imprinted, unwittingly, on an object’s surface.
In traditional matching scenarios where plain or rolled prints are compared against one another, the larger surface area translates into more discriminative information for matching. For forensic applications, the larger surface area in the reference image increases the likelihood of obtaining sufficient overlap when matching partial latent fingerprints. Plain prints are easy to collect and typically don’t require operator assistance to produce good quality images for matching. While N2N prints provide superior information for matching, they are more difficult to collect than plain and require physically rolling the finger across a flat surface, often requiring assistance from a human operator. Having an operator involved in the process constrains the feasibility of collecting rolled prints in a variety of environments and operational scenarios. Removing the human-in-the-loop from this process through advanced collection and processing techniques will allow better fingerprint data to be collected, leading to improved recognition performance, while reducing the time and cost of collection.
Awards:- $295,000 in prizes