title: Quantifying the weight of fingerprint evidence through the spatial relationship, directions and types of minutiae observed on fingermarks.
abstract: This paper presents a statistical model for the quantification of the weight of fingerprint evidence. Contrarily to previous models (generative and score-based models), our model proposes to estimate the probability distributions of spatial relationships, directions and types of minutiae observed on fingerprints for any given fingermark. Our model is relying on an AFIS algorithm provided by 3M Cogent and on a dataset of more than 4,000,000 fingerprints to represent a sample from a relevant population of potential sources. The performance of our model was tested using several hundreds of minutiae configurations observed on a set of 565 fingermarks. In particular, the effects of various sub-populations of fingers (i.e., finger number, finger general pattern) on the expected evidential value of our test configurations were investigated. The performance of our model indicates that the spatial relationship between minutiae carries more evidential weight than their type or direction. Our results also indicate that the AFIS component of our model directly enables us to assign weight to fingerprint evidence without the need for the additional layer of complex statistical modeling involved by the estimation of the probability distributions of fingerprint features. In fact, it seems that the AFIS component is more sensitive to the sub-population effects than the other components of the model. Overall, the data generated during this research project contributes to support the idea that fingerprint evidence is a valuable forensic tool for the identification of individuals.
authors: The South Dakota State University, Department of Mathematics and Statistics, Harding Hall, Brookings, SD 57007, United States; The Pennsylvania State University, Department of Statistics, University Park, PA 16802, United States; Two N's Forensics Inc., Brookings, SD 57006, United States. Electronic address: Cedric.Neumann@me.com.
authors: Cedric
authors: C
authors: Neumann
authors: School of Criminal Justice, Forensic Science Institute, University of Lausanne, Batochime, quartier Sorge, CH-1015 Lausanne-Dorigny, Switzerland.
authors: Christophe
authors: C
authors: Champod
authors: The Pennsylvania State University, Department of Statistics, University Park, PA 16802, United States.
authors: Mina
authors: M
authors: Yoo
authors: School of Criminal Justice, Forensic Science Institute, University of Lausanne, Batochime, quartier Sorge, CH-1015 Lausanne-Dorigny, Switzerland.
authors: Thibault
authors: T
authors: Genessay
authors: Elite Forensic Services, LLC, Saint-Paul, MN 55117, United States.
authors: Glenn
authors: G
authors: Langenburg
mesh_terms: D003878:Dermatoglyphics; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D062206:Spatial Analysis
keywords: Fingerprint evidence; Spatial relationship; Statistical model; Strength of evidence; Sub-population effect