Interoperability between Fingerprint Biometric Systems: An Empirical Study

Abstract

Abstract-Fingerprints are likely the most widely used biometric in commercial as well as law enforcement applications. With the expected rapid growth of fingerprint authentication in mobile devices their importance justifies increased demands for dependability. An increasing number of new sensors, applications and a diverse user population also intensify concerns about the interoperability in fingerprint authentication. In most applications, fingerprints captured for user enrollment with one device may need to be "matched" with fingerprints captured with another device. We have performed a large-scale study with 494 participants whose fingerprints were captured with 4 different industry-standard optical fingerprint devices. We used two different image quality algorithms to evaluate fingerprint images, and then used three different matching algorithms to calculate match scores. In this paper we present a comprehensive analysis of dependability and interoperability attributes of fingerprint authentication and make empirically-supported recommendations on their deployment strategies.

Alignment-Free Cross-Sensor Fingerprint Matching Based on the Co-Occurrence of Ridge Orientations and Gabor-HoG Descriptor

“…The study's outcomes show that false non-match rates for fingerprint-matching systems are affected by the diversity of the capture devices but that false match rates are not. Mason et al [11] proposed an approach to minimize the effects of low interoperability between optical sensors by combining some extracted fingerprint features with match scores using a classifier. The selected feature vector extracted from a fingerprint contained the following measures: average gray level, contrast, minutia count, quality measures, photo response non-uniformity (PRNU) noise, first-order statistics, mean of the orientation coherence matrix, and device ID.…”

“…8 shows box plots of the inter-ridge distances for each dataset of the FingerPass database. The ridge spacing is in the range [5][6][7][8][9][10][11]. We argue that choosing the value of d to reflect the inter-ridge distance will improve the robustness.…”

Supporting Decision-Making for Biometric System Deployment through Visual Analysis

“…Fingerprints were acquired using four Live-scan devices (D0 -D3) 1 . The devices are widely used in industry and hence representative of common real world installations.…”

“…A good biometric system minimises both of these types of failures, but there is a clear trade-off during configuration as attempting to minimise the probability of one type of failure, increase the probability of another type of failure occurring. We have previously shown [1] that the decision on where to set the threshold seems to be highly influenced by several factors, including the type of devices that have been used to capture the images, the image quality, the matcher that has generated the scores, the gender and age of the identity claimant etc. In order to inform our design and the analysis process we employ, we identify a number of questions (tasks) to be addressed that are critical in making decisions in deploying biometric systems:…”

“…Recently, we have provided a comprehensive analysis of the effects of interoperability on the overall dependability of the fingerprint matchers [1]. One limitation we identified during this analysis is that system deployment teams may have a number of questions with system performance implications that are not covered in ours or similar studies.…”