Abstract
We study the impact of minutiae errors in the performance of latent fingerprint identification systems. We perform several experiments in which we remove ground-truth minutiae from latent fingerprints and evaluate the effects on matching score and rank-n identification using two different matchers and the popular NIST SD27 dataset. We observe how missing even one minutia from a fingerprint can have a significant negative impact on the identification performance. Our experimental results show that a fingerprint which has a top rank can be demoted to a bottom rank when two or more minutiae are missed. From our experimental results, we have noticed that some minutiae are more critical than others to correctly identify a latent fingerprint. Based on this finding, we have created a dataset to train several machine learning models trying to predict the impact of each minutia in the matching score of a fingerprint identification system. Finally, our best-trained model can successfully predict if a minutia will increase or decrease the matching score of a latent fingerprint.
Highlights
In the last two decades, machine learning has attracted increasing interest in the research community, eager to automatize several processes in different application areas [1,2].One of these areas is Biometrics [3,4,5,6,7,8,9], which aims to use several unique characteristics of the human body to automatically verify or identify people
Aiming to develop methods that can help latent fingerprint examiners make better decisions with automatic methods as well as respond to the research questions mentioned above, in this paper, we propose to study how human error during minutiae extraction can affect the identification of latent fingerprints
Aiming to develop automatic methods that can help latent fingerprint examiners make better decisions and can enable quality-based fingerprint processing, we carried out a study of how minutiae errors can affect the identification of latent fingerprints
Summary
In the last two decades, machine learning has attracted increasing interest in the research community, eager to automatize several processes in different application areas [1,2]. In 2006, in response to a misidentification of a latent fingerprint in a high profile case, the Federal Bureau of Investigation commissioned a review committee to evaluate the current state of forensic latent fingerprint identification technologies and processes and to better understand the bases of this discipline [24]. Forensic latent fingerprint identification is usually performed through semi-automatic processes where human participation is still critical (e.g., minutiae labeling). Aiming to develop methods that can help latent fingerprint examiners make better decisions with automatic methods as well as respond to the research questions mentioned above, in this paper, we propose to study how human error during minutiae extraction can affect the identification of latent fingerprints.
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