A methodology to estimate the time of latent fingerprint deposition would be of great value to law enforcement and courts. It has been observed that ridge topography changes as latent prints age, including the widths of ridges that could be measured as a function of time. Crime suspects are commonly identified using fingerprint databases that contain reference inked tenprints (flat and rolled impressions). These can be of interest in aging studies as they provide baseline information relating to the original (nonaged) ridges' widths. In practice, the age of latent fingerprints could be estimated following a comparison process between the evidentiary aged print and the corresponding reference inked print. The present article explores possible correlations between inked and fresh latent fingerprints deposited on different substrates and visualized with TiO . The results indicate that the ridge width of flat inked prints is most similar to fresh latent fingerprints and these should be used as the comparison standard for future aging studies.

In most latent fingermark aging studies, two-dimensional (2D) features are obtained from photo images, scans, or inked impressions. However, some relevant information is possibly being missed because fingermarks are three-dimensional (3D) objects that age in all three dimensions. A feature that has not been carefully examined is how the height of ridges changes over time. In this report, a 3D imaging technology-called optical profilometry-is introduced as a tool for the visual examination of the aging process. Optical profilometry is a nondestructive technology that allows the visualization and data acquisition of unprocessed latent fingermarks. Detailed ridge images and spatiotemporal data were successively obtained on the x-, y- and z-axis, delivering 3D topographical information. OP was able to detect the loss of ridge heights over time. The feasibility of employing this technology to collect data on the aging process of ridges has been proven.

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The D‐optimality criterion is often used in computer‐generated experimental designs when the response of interest is binary, such as when the attribute of interest can be categorized as pass or fail. The majority of methods in the generation of D‐optimal designs focus on logistic regression as the base model for relating a set of experimental factors with the binary response. Despite the advances in computational algorithms for calculating D‐optimal designs for the logistic regression model, very few have acknowledged the problem of separation, a phenomenon where the responses are perfectly separable by a hyperplane in the design space. Separation causes one or more parameters of the logistic regression model to be inestimable via maximum likelihood estimation. The objective of this paper is to investigate the tendency of computer‐generated, nonsequential D‐optimal designs to yield separation in small‐sample experimental data. Sets of local D‐optimal and Bayesian D‐optimal designs with different run (sample) sizes are generated for several “ground truth” logistic regression models. A Monte Carlo simulation methodology is then used to estimate the probability of separation for each design. Results of the simulation study confirm that separation occurs frequently in small‐sample data and that separation is more likely to occur when the ground truth model has interaction and quadratic terms. Finally, the paper illustrates that different designs with identical run sizes created from the same model can have significantly different chances of encountering separation.

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