Automatic assessment of fingermarks quality: Exploration of the possible application in the context of detection and comparison with human examiners

Abstract

In forensic science, particularly in the context of latent fingermarks detection, forensic scientists are often faced with the need to assess the quality of the detected fingermarks to quantitatively interpret their results and express conclusions. Today this process is mainly carried out by human examiners referring to guidelines or provided quality scales. The largest the set of fingermarks (e.g., hundreds, thousands), the longest and the most labor-intensive this task becomes. Moreover, it is difficult to guarantee a fully objective process since the subjectivity of each individual is almost impossible to avoid, especially with regards to the interpretation of the quality scale levels or when facing fingermarks detected in an inhomogeneous manner. In this paper, the possibility of automatizing the quality assessment step is explored.The choice has been made to consider the use of quality assessment algorithms currently applied in an identification context. 150 natural fingermarks from ten donors were deposited on three different supports. These marks were detected using 1,2-indanedione/zinc or cyanoacrylate fumigation depending on the support. Then, their quality was assessed by five examiners, according to the UNIL scale, and by seven algorithms (i.e., Lights Out, Latent Fingerprint Image Quality 1 and 2, Latent Quality Metric, Expected Score Likelihood Ratio, NIST Fingerprint Image Quality, MINDTCT).Spearman and Pearson correlations were calculated, and the distribution of scores for each algorithm was charted (using boxplots) against the results provided by the human examiners. The most promising results were obtained with the LQM algorithm, more specifically with the fingermark clarity metric.

Evaluation of the loss of fingermark ridge clarity as a function of biological sex

“…The fingermark quality was manually assessed using a standard Quality Score (QS) scale (Table 1) [16][17][18] prior to proceeding with the automated Universal Latent Workstation (ULW) software (Version 6.6-2015, Federal Bureau of Investigation) analysis. On the QS grading scale, fingermarks with scores "1" and "2" were deemed unsuitable for identification purposes whereas scores "3" and "4" were deemed suitable by the examiner.…”

“…The LQM examined the relative extension of colors on the fingermark and provided the area extension of each color based upon the number of pixels that remained within identifiable areas [20]. The LQM metric may be used to minimize the level of subjectivity of examiners when evaluating the quality of the latent fingermark [16,17,21]. The blue-and green-coded areas represented good ridge clarity areas, whereas yellow and red depicted the lowest qualities with unclear ridges and minutiae [16,17].…”

“…The LQM metric may be used to minimize the level of subjectivity of examiners when evaluating the quality of the latent fingermark [16,17,21]. The blue-and green-coded areas represented good ridge clarity areas, whereas yellow and red depicted the lowest qualities with unclear ridges and minutiae [16,17]. For the purpose of this research, the area extensions of blue and green coloration were combined to act as one unique metric, i.e., "Blue+Green".…”

“…As the series progressed, a gradual loss in ridge clarity was noted as well as a widening of the variability (quartiles and whiskers). Depositions S4 to S6 displayed the widest range of TA B L E 1 Grading scale based on four standard visual Quality Scores (QS) and the analogous ULW color-codes for the analysis of ridge quality/clarity of latent fingermarks [16][17][18] variability when compared with S1 to S3. This boxplot revealed that at S3 or S4 there could be a major "inflection point" where a substantial loss in ridge quality could occur.…”

“…Once enhanced, fingermarks are usually photographed, edited, and (for the purpose of this research) uploaded into the Universal Latent Workstation (ULW) software for ridge quality assessment [14,15]. A more common practice is to assign an initial Quality Score (QS) value [16][17][18] to the fingermarks, which is essential to evaluating whether they could be suitable for comparisons. QS involves a visual inspection by the examiner under low magnification, whereas ULW is an objective and software-driven tool that provides key information on the visual quality and clarity of friction ridges.…”