# Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction
## 1 Introduction
Recent studies [21; 30] have shown that automatic fingerprint identification methods based on minutiae are often readily applicable and commonly outperform other types of methods. However, latent fingerprint identification rates are as low as 71.32% for Rank-1 when using medium-size databases (100,000 fingerprints) [30]. Consequently, latent fingerprint identification is an area of constant evolution, which aims to find new methods that help obtain better identification rates. Based on the above discussion, three main research questions arise:  
* What is the impact of human errors in the performance of Automatic Fingerprint Identification Systems? Figure 1: Example of a latent fingerprint and its corresponding impression, taken from the database NIST SD27 [23]. Notice how the latent fingerprint is a low-quality image, containing background noise that interrupts ridge flow. On the contrary, the impression is a clear and high-quality image taken under controlled conditions. Are there minutiae having a higher impact on the identification performance when missed? If so, is there any feature of a minutia that can help determine its importance? 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. By understanding the effect these mistakes have on matcher performance, we can identify research areas related to developing methods that seek to aid the latent fingerprint examiners. The main contribution of this paper are summarized as follows:  
To the best of our knowledge, this is the first study related to the matching algorithms' performance when minutiae are missed from latent fingerprints. We quantify and discuss the impact of missing minutiae in the latent fingerprint on two latent fingerprint matching algorithms' performance by removing manually marked minutiae from latent fingerprints and calculating their matching score and rank, and comparing them to the original ground-truth latent fingerprints. By removing minutiae from latent fingerprints, we simulate human mistakes. We show that even a single mistake could be fatal to the process. The variation in matching score when removing different minutiae leads us to believe that some minutiae are more important than others when it comes to latent fingerprint identification [13]. If we were able to determine the specific features of minutia that have a high impact on matching score, we could develop methods to aid latent fingerprint examiners by marking regions [31] where a mistake in feature selection would have a high negative impact in matching, in a kind of fingerprint quality map [18; 32]. The remainder of this paper is organized as follows. In Section 2, we analyze previous works. After, in Section 3, we state the role of human errors in the forensic fingerprint analysis. Next, in Section 4, we define all our experimental setup. Then, in Section 5, we evaluate the impact of minutiae errors in latent fingerprint identification. Then, in Section 6, we experiment on predicting the utility of given minutiae for latent fingerprint identification. Finally, in Section 7, we present our conclusions and future work.