Use of automated quality assessment algorithms in fingermark detection research - Application to IND/Zn vs DFO.

When developing detection techniques for fingermarks, the detected fingermarks must be evaluated for their quality to assess the effectiveness of the new method. It is a common practice to compare the performance of the new (optimized) technique with the traditional or well-established ones. In current practice, this evaluation step is carried out by a group of human assessors. A new approach is applied in this paper and consists of using algorithms to perform this task. To implement this approach, the comparison between IND/Zn and DFO has been chosen because it has already been the subject of many articles published in recent years and a consensus exists on the superiority of IND/Zn over DFO. The quality of 3'600 fingermarks developed using both detection techniques was assessed automatically using two algorithms: LQM (Latent Quality Metric) and ILFQM (Improved Latent Fingerprint Quality Metric). The distribution of quality scores was studied for both detection techniques. The results showed that fingermarks detected with IND/Zn received higher scores on average than fingermarks detected with DFO, which is in line with the consensus in the literature based on human assessment. The results of this research are promising and shows that automated fingermark quality assessment is an efficient and viable way to comparatively assess fingermark detection techniques.

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

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.