- What is the impact of human errors in the performance of Automatic Fingerprint Identification Systems?
- 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?
- 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.
2. Previous Works
2.1. Research on the Performance of Experts in Latent Fingerprint Analysis
2.2. Impact of Fingerprint Variations in Automatic Fingerprint Recognition
3. Forensic Fingerprint Analysis: Role of Human Errors
- Reveal: in this stage, an expert should use specific powders, based on the surface and shape of the object, to reveal the latent fingerprint. The errors one may incur at this stage are:
- Using a revealing powder that does not correspond to the surface or shape of the object where the latent fingerprint is, which could wrongly reveal the ridges of the latent fingerprints.
- Using more revealing powder than necessary, creating a filling among the fingerprint ridges, and consequently, making it hard to be of use on the next stages.
- Capture: in this stage, an expert must be careful at capturing the latent fingerprint under the best possible conditions. Errors that are typically observed at this stage are:
- While lifting the latent fingerprint, deformations might be created in the ridges creating false bifurcations or false ends on the ridges. As a result, errors are induced that may severely affect the feature extraction stage.
- Output an out of focus or low-resolution photo of the latent fingerprint.
- Lack of use of a rule next to the latent fingerprint that may enable one to estimate the real scale of the fingerprint.
- Feature extraction: during this stage, the examiners will extract all features necessary for the identification stage. The more common flaws one may experience at this stage are:
- False features can be added from errors originated at the capture stage.
- Actual features could be missed by examiners.
- The position or angle of some features could be shifted due to human perception.
- Identification: any flaw committed at previous stages will affect identification performance. However, the main errors that may show up at this stage are:
- The ranking provides the corresponding impression, but the experts obviate this matching.
- The ranking does not provide the corresponding impression due to human errors issued at the previous stages.
- The expert issues a true positive identification when really it is a false positive.
4. Materials and Methods
- Minutia Cylinder-Code (MCC) : Matching algorithm based on a three-dimensional representation constructed using basic minutiae features such as angles and distances to other minutiae.
- Deformable Minutiae Clustering using Cylinder-Codes (DMCCC) : A matching algorithm independent of minutiae descriptors based on the use of clustering to improve robustness to non-linear transformations. In this case, we use Minutia Cylinder-Code as the minutiae descriptor.
- The first set of experiments consists of randomly removing minutiae from latent fingerprints in the database and comparing the resulting CMC curves in a closed set comparison. We ran 35 experiments; the first 20 experiments consisted of removing a fixed number of minutiae from each latent fingerprint, from 1 to 20. The next 15 experiments consisted of removing a percent of minutiae from each latent fingerprint, from to . Each experiment was run 10 times with different randomly-selected removed minutiae, and the results of the 10 experiments are then averaged.
- The second set of experiments aims to measure the matching algorithms’ negative impact when minutiae are removed. We remove every possible combination of one, two, and three minutiae from every fingerprint (more than 1,100,000 combinations for all fingerprints in the NIST SD27) for this set of experiments. Every new fingerprint (with the removed minutiae) was tested with the two selected matching algorithms to obtain their matching score and compared with the matching score obtained from the original fingerprints (without removed minutiae).
- The third set of experiments consists of determining the set of minutiae that less decreased the score and those that decreased the most the score. Using our second experiment results, we selected six combinations of minutiae for each fingerprint and each matching algorithm to see how the change in score is reflected in the CMC curve and the rank-100 identification. The three combinations which lowered the score the most are considered the “lower” class. The three combinations which lowered the score the least are considered the “higher” class.
- Finally, we perform one last experiment aimed to determine if it is possible to predict if removing a minutia will have a positive or negative effect on the matching score.
5. Evaluating the Impact of Minutiae Errors
6. Predicting the Impact of Minutiae Errors
- d[1–6]: Each of these features has the distance from the minutia to the closest minutia (d), to the second closest minutia (d), and so on.
- r[15, 30, 45, 60, 75, 90]: Each of these features counts how many minutiae there are in a radius (of 15 pixels, 30 pixels, and so on) around the specified minutia.
- class: Either a positive or negative impact on the matching score.
7. Conclusions and Future Work
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
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Short Biography of Authors
|Octavio Loyola-González received his Ph.D. degree in Computer Science from the National Institute for Astrophysics, Optics, and Electronics, Mexico, in 2017. He has won several awards from different institutions due to his research work on applied projects; consequently, he is a Member of the National System of Researchers in Mexico (Rank1). He worked as a distinguished professor and researcher at Tecnologico de Monterrey, Campus Puebla, for undergraduate and graduate programs of Computer Sciences. Currently, he is responsible for running Machine Learning and Artificial Intelligence practice inside Altair Management Consultants Corp., where he is involved in the development and implementation using analytics and data mining in the Altair Compass department. He has outstanding experience in the fields of big data and pattern recognition, cloud computing, IoT, and analytical tools to apply them in sectors where he has worked for as Banking and Insurance, Retail, Oil and Gas, Agriculture, Cybersecurity, Biotechnology, and Dactyloscopy. From these applied projects, Dr. Loyola-González has published several books and papers in well-known journals, and he has several ongoing patents as a manager and researcher in Altair Compass.|
|Emilio Francisco Ferreira Mehnert obtained his Bachelor’s of Engineering degree in Software Engineering from Tecnológico de Monterrey, Campus Santa Fe in 2017. He received his Master of Science degree in Computer Science from Tecnológico de Monterrey, Campus Estado de México in 2020. His interests include machine learning, deep learning, and software development.|
|Aythami Morales Moreno received his M.Sc. (Electronical Engineering) and Ph.D. (Artificial Intelligence) degrees from Universidad de Las Palmas de Gran Canaria in 2006 and 2011, respectively. Since 2017, he is an Associate Professor with the Universidad Autonoma de Madrid. He has conducted research stays at Michigan State University, Hong Kong Polytechnic University, University of Bologna, and the Schepens Eye Research Institute. He has authored over 100 scientific articles in topics related to machine learning, trustworthy AI, and biometric signal processing.|
|Julian Fierrez (Member, IEEE) received his M.Sc. and the Ph.D. degrees in Telecommunications Engineering from Universidad Politecnica de Madrid, Spain, in 2001 and 2006, respectively. Since 2004, he is at Universidad Autonoma de Madrid, where he is an Associate Professor since 2010. His research is on signal and image processing, AI fundamentals and applications, HCI, forensics, and biometrics for security and human behavior analysis. He is actively involved in large EU projects in these topics (e.g., BIOSECURE, TABULA RASA and BEAT in the past; now IDEA-FAST, PRIMA, and TRESPASS-ETN). Since 2016, he is an Associate Editor for Elsevier’s Information Fusion and IEEE Trans. on Information Forensics and Security, and since 2018 also for IEEE Trans. on Image Processing. He has been a General Chair of IAPR CIARP 2018 and IAPR IbPRIA 2019. Since 2020, he is member of the ELLIS Society. Prof. Fierrez has received best papers awards at AVBPA, ICB, IJCB, ICPR, ICPRS, and Pattern Recognition Letters. He is also a recipient of several world-class research distinctions, including: EBF European Biometric Industry Award 2006; EURASIP Best Ph.D. Award 2012; Miguel Catalan Award to the Best Researcher under 40 in the Community of Madrid in the general area of Science and Technology; and IAPR Young Biometrics Investigator Award 2017, given to a single researcher worldwide every two years under the age of 40 whose research has had a major impact in biometrics.|
|Miguel Angel Medina-Pérez received a Ph.D. in Computer Science from the National Institute of Astrophysics, Optics, and Electronics, Mexico, in 2014. He is currently a Research Professor with the Tecnologico de Monterrey, Campus Estado de Mexico, where he is also a member of the GIEE-ML (Machine Learning) Research Group. He has rank 1 in the Mexican Research System. His research interests include pattern recognition, data visualization, explainable artificial intelligence, fingerprint recognition, and palmprint recognition. He has published tens of papers in referenced journals, such as “Information Fusion,” “IEEE Transactions on Affective Computing,” “Pattern Recognition,” “IEEE Transactions on Information Forensics and Security,” “Knowledge-Based Systems,” “Information Sciences,” and “Expert Systems with Applications.” He has extensive experience developing software to solve pattern recognition problems. A successful example is a fingerprint and palmprint recognition framework which has more than 1.3 million visits and 135 thousand downloads.|
|Raúl Monroy obtained a Ph.D. degree in Artificial Intelligence from Edinburgh University, in 1998, under the supervision of Prof. Alan Bundy. He has been in Computing at Tecnologico de Monterrey, Campus Estado de México, since 1985. In 2010, he was promoted to (full) Professor in Computer Science. Since 1998, he is a member of the CONACYT-SNI National Research System, rank three. Together with his students and members of his group, Machine Learning Models (GIEE – MAC), Prof. Monroy studies the discovery and application of novel model machine learning models, which he often applies to cybersecurity problems. At Tecnologico de Monterrey, he is also the Head of the graduate program in computing, at region CDMX.|
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