Interoperability of fingerprint sensors and matching algorithms

Biometric systems are widely deployed in governmental, military and commercial/civilian applications. There are a multitude of sensors and matching algorithms available from different vendors. This creates a competitive market for these products, which is good for the consumers but emphasizes the importance of interoperability. In fingerprint recognition, interoperability is the ability of a system to work with a diverse set of fingerprint devices. Variations induced by fingerprint sensors include image resolution, scanning area, gray levels, etc. Such variations can impact the quality of the extracted features, and cross-device matching performance. This is true even when dealing with fingerprint sensors of the same sensing technology. In this thesis, we perform a large-scale empirical study of the status of interoperability between fingerprint sensors and assess the performance consequence when interoperability is lacking. Additionally we develop a method to increase interoperability in fingerprint-based recognition systems deploying optical fingerprint sensors. A set of features to measure differences in fingerprint acquisition is designed and evaluated. Finally, different fusion schemes based on machine learning are tested end evaluated in order to exploit the designed set of features. Experimental results show that the proposed approach is able to reduce cross-device match error rates by a significant margin

The Assessment of Fingerprint Quality for a More Effective Match Score in Minutiae-Based Matching Performers

One of the most common types of evidence recovered from a crime scene are latent fingerprints, however these impressions are often of low quality. The quality of a latent fingerprint is described as the degree to which the ridge details can be observed. If the quality of the latent fingerprint is very clear, a minutiae-based matching algorithm with automatic extraction may detect and utilize the minutiae that are truly present in the fingerprint. However, if the impression is of poor quality, the minutiae-based matching algorithm\u27s automatic extraction may detect fewer features and could completely miss features resulting in the return of an unrelated candidate. The aim of this research was to determine a method to improve the match score of latent fingerprints by removing the bad quality regions, where both a subjective and objective methods were utilized. The subjective method utilized the predetermined quality categories of good, bad or ugly to assign a latent fingerprint. After classification, each impression was processed by AdobeRTM PhotoshopRTM and four quality areas were serially removed. In the objective method, each latent fingerprint was assessed with NFIQ algorithm and then MINDTCT algorithm. The MINDTCT algorithm provided a quality map that was used to remove successive portions of each latent fingerprint. The resulting new images from both methods were compared to a database using the two different minutiae-based matching algorithms: AFIX TrackerRTM and BOZORTH3.;The results were examined utilizing the statistical methods of receiver operator characteristic (ROC) curves, area under the ROC curve (AUC), cumulative match characteristic (CMC) curve, Wilcoxon signed-rank test, Spearman\u27s rank correlation and the comparison of the removal methods. ROC curves and the resulting AUC were able to determine that the AFIX TrackerRTM program is a reliable performer with high AUC values, while the BOZORTH3 minutiae-based algorithm did not perform well with low AUC scores of around 0.5. The results produced from the CMC curves showed that the subjective method produced higher rank 1 and top 10 rank identification than the objective method, contrary to what was hypothesized. The correlation scores showed the manual and automatic extraction were weakly correlated to one another. However, a very weak to no correlation between the algorithms of the BOZORTH3 and AFIX Tracker R was observed. The comparison between the subjective and objective methods of removal showed the examiner allowed for a more conservative removal of the fingerprint than the objective method. With this result in connection with the CMC curve results shows that being more conservative produces higher rank 1 and top ten rank identification scores

Fingerprint Recognition for Children

This document is the final report of the Fingerprint Recognition Study of Children below the Age of 12 Years . The key findings of the study are: • Growth has limited influence on fingerprint recognition • Size (in terms of the dimensions of the relevant fingerprint characteristics) does not constitute any theoretical barrier for automated fingerprint recognition. • Image quality (in terms of low contrast and distortion effects) is the ultimate problem for child fingerprints, and image quality is strongly influenced by size. • Relevant quality metrics for fingerprints need revision with regard to the children case. • Isotropic growth model may serve as a good approximation to cover changes over time. • Alternative acquisition devices for fingerprints should be seriously considered in the future. Apart from these major findings, the report provides a number of recommendations to improve the process of acquiring fingerprints from children.JRC.G.7-Digital Citizen Securit

The Application of the Human-Biometric Sensor Interaction Method to Automated Border Control Systems

Biometrics components are used in many different systems and technologies to verify that the user is whom they say they are. In Automated Border Control systems, biometrics components used in conjunction with a traveller's documents to make sure the user is whom they say they are so that they can cross into a countries borders. The systems are expected to verify the identity with a higher degree than officers who manually check travellers. Each year the number of travellers crossing through a country borders increases and so systems are expected to handle bigger demands; through improving the user experience to ensuring accuracy and performance standards increase. While the system does bring its benefits through increased speed and higher security, there are drawbacks. One of the main issues with the systems is a lack of standardisation across implementations. Passing through an automated process at Heathrow may be different to Hong Kong. The infrastructure, information, environment and guidance given during the transaction will all greatly differ for the user. Furthermore, the individual components and subsequent processing will be evaluated using a different methodology too. This thesis reports on the contrasts between implementations, looking at solutions which utilise different biometric modalities and travel documents. Several models are devised to establish a process map which can be applied to all systems. Investigating further, a framework is described for a novel assessment method to evaluate the performance of a system. An RGB-D sensor is implemented, to track and locate the user within an interactive environment. By doing so, the user's interaction is assessed in real-time. Studies then report on the effectiveness of the solution within a replicated border control scenario. Several relationships are studied to improve the technologies used within the scenario. Successful implementation of the automated assessment method may improve the user's experience with systems, improving information and guidance, increasing the likelihood of successful interaction while maintaining a high level of security and quicker processing times

Investigating the Impact of Demographic Factors on Contactless Fingerprint Interoperability

Improvements in contactless fingerprinting have resulted in contactless fingerprints becoming a faster and more convenient alternative to contact fingerprints. The interoperability between contactless fingerprints and contact fingerprints and how demographic factors can change interoperability has been challenging since COVID-19; the need for hygienic alternatives has only grown because of the sudden focus during the pandemic. Past work has shown issues with the interoperability of contactless prints from kiosk devices and phone fingerprint collection apps. Demographic bias in photography for facial recognition could affect photographed fingerprints. The paper focuses on evaluating match performance between contact and contactless fingerprints and evaluating match score bias based on five skin demographics; melanin, erythema, and the three measurements of the CIELab color space. The interoperability of three fingerprint matchers was tested. The best and worst Area Under the Curve (AUC) and Equal Error Rate (EER) values for the best-performing matcher were an AUC of 0.99398 and 0.97873 and an EER of 0.03016 and 0.07555, respectively, while the best contactless AUC and EER were 0.99337 and 0.03387 indicating that contactless match performance can be as good as contact fingerprints depending on the device. In contrast, the best and worst AUC and EER for the cellphone contactless fingerprints were an AUC of 0.96812 and 0.85772 and an EER of 0.08699 and 0.22130, falling short of the lowest performing contact fingerprints. Demographic analysis was on the top two of the three matchers based on the top one percent of non-match scores. Resulting efforts found matcher-specific bias for melanin showing specific ranges affected by low and high melanin values. While higher levels of erythema and general redness of the skin improved performance. Higher lightness values showed a decreased performance in the top-performing matcher