Predicting suitability of finger marks using machine learning techniques and examiner annotations.

Previous research has established the variability of examiners in reaching suitability determinations for friction ridge comparisons. Attempts to create predictive models to assist in this determination have been made, but have been largely confined to fully automated processes that focus on suitability for AFIS entry. This work develops, optimizes, and validates a hybrid predictive model that utilizes both examiner-observed variables and automated measures of quality and rarity to arrive at suitability classifications along four scales that have been proposed in our previous research: Value, Complexity, AFIS, and Difficulty. We show that a model based only on automatically extracted quality or selectivity measures does not perform as well as when used in conjunction with a limited set of user inputs. The model is then based on a limited set of input from the users while taking advantage of automatic measures with a view to limit the user encoding effort while maintaining accuracy. The developed model is able to make predictions at up to 83.13% accuracy when using full study data and maintains similar levels of accuracy in an external validation study. The model achieved accuracy at a similar level to that of examiners asked to make the same suitability determinations across all scales. The model can easily be introduced into an operational laboratory with very little additional operational burden to provide guidance on suitability, complexity, AFIS, and quality assurance decisions; to assist in designing testing and training exercises of progressive difficulty; to describe the difficulty of a mark in testimony; and to provide a consensus-based opinion in laboratories where a second opinion is desired but the laboratory lacks sufficient personnel to form a consensus panel.