Determination of AFIS "sufficiency" in friction ridge examination

Fingerprints have been used with considerable success over the past century in multiple civil government, law enforcement and criminal investigation applications. Since the 1970s, computer assisted systems (AFIS - Automated Fingerprint Identification System) have been increasingly used to automatically compare fingerprints and propose associations between multiple friction ridge impressions of known or unknown sources. AFIS were initially entirely subordinated to human examiners. Improvements in the matching algorithms and workload considerations have pushed agencies to implement completely automated processes, known as "Lights-out" modes, where AFIS's render unsupervised conclusions on the donors of the queries. Such fully automated process is common for tenprint-to-tenprint comparisons; however it is currently not being widely adopted for other types of comparisons, such as latent print-to-tenprint comparisons. In this paper, we explore a statistical model that can be used to facilitate the latent print examination workflow by predicting whether any latent print should be searched in AFIS. In particular, we are interested in preventing poor quality latent prints to be searched in vain, and thus unnecessarily consume resources. Ultimately, we show that our model could be used to efficiently manage workflow and workload by categorizing latent prints as a function of the quality and quantity of information that can be observed on them, which enables examiners to select the most appropriate examination and quality assurance processes for each print.