# Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction
## 1 Introduction  
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. From the several physiological traits of the human body, fingerprints hold the label of being the most reliable due to its uniqueness, permanence, acceptability, and collectability [2, 3, 5, 10, 11]. Fingerprints are considered genetically unique; up to now, there have been no reports of two individuals having the same fingerprint. Therefore, this biometric feature is widely used in several application fields, such as criminology, banking, and healthcare [2, 12, 13, 14, 15]. Fingerprints can be classified into impressions and latent fingerprints [6, 7, 16, 17]. Fingerprint impressions are acquired under controlled conditions and have high quality. By contrast, latent fingerprints are unintentionally left by someone when manipulating objects and have a low quality [18] (see Figure 1). Similarly, the methodologies for comparing fingerprints have been clustered into two groups. On the one hand, fingerprint verification, which aims to verify an individual's identity from two given impressions in a fully automatized process. On the other hand, fingerprint identification, which aims to search a background database for impressions that are the most similar to a given latent fingerprint query usually related to forensic applications [12, 19, 20, 21, 22]. 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]. Their results showed that there are scientific areas where improvements in the practice can be made, particularly regarding validation, the analysis, comparison, evaluation, and verification (ACE-V) process, and data collection. Fingerprint verification technologies based on digital impressions are dominated by fully automatic systems (e.g., smartphone authentication sensors). However, forensic latent fingerprint identification is usually performed through semi-automatic processes where human participation is still critical (e.g., minutiae labeling). Latent fingerprints are important evidence in thousands of real prosecutions all around the world. Together with DNA, the latent fingerprint is the most important biometric identification trait in these prosecutions. The main stages of latent fingerprint analysis are (a) reveal; (b) capture; (c) feature extraction, and (d) identification or matching. In nowadays forensic analysis, stages a to c are usually performed by human experts [25] while d is performed by automatic algorithms. These semi-automatic processes are error-prone; errors may originate either from a human or an algorithm. The analysis of these errors is critical to understand the performance of these systems. There are studies analyzing the likelihood of human errors in the stages a to c and showing these errors are habitual [26; 27; 28; 29]. Nevertheless, there are not studies evaluating how these errors affect the identification stage, (d). Minutiae extraction is a critical stage for any latent fingerprint identification system [13]. Usually, Automated Fingerprint Identification Systems (AFIS) work adequately for extracting minutiae from impressions but not for latent fingerprints. As a result, human fingerprint examiners are needed for extracting minutiae from latent fingerprints [21; 22]. However, failing at marking minutiae or marking spurious minutiae can lead to misidentifying latent fingerprints, releasing criminals, or, even worse, yielding the apprehension of innocent people [21].