A Large-Scale Study of Fingerprint Matching Systems for Sensor Interoperability Problem

Abstract

The fingerprint is a commonly used biometric modality that is widely employed for authentication by law enforcement agencies and commercial applications. The designs of existing fingerprint matching methods are based on the hypothesis that the same sensor is used to capture fingerprints during enrollment and verification. Advances in fingerprint sensor technology have raised the question about the usability of current methods when different sensors are employed for enrollment and verification; this is a fingerprint sensor interoperability problem. To provide insight into this problem and assess the status of state-of-the-art matching methods to tackle this problem, we first analyze the characteristics of fingerprints captured with different sensors, which makes cross-sensor matching a challenging problem. We demonstrate the importance of fingerprint enhancement methods for cross-sensor matching. Finally, we conduct a comparative study of state-of-the-art fingerprint recognition methods and provide insight into their abilities to address this problem. We performed experiments using a public database (FingerPass) that contains nine datasets captured with different sensors. We analyzed the effects of different sensors and found that cross-sensor matching performance deteriorates when different sensors are used for enrollment and verification. In view of our analysis, we propose future research directions for this problem.

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

“…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.…”

“…Recent research demonstrated the significance of studying the effect of using different fingerprint sensors on automatic fingerprint-matching [5]. Jain and Ross [1] proved that the performance of a matching system decreases drastically when fingerprints are captured with two different sensors.…”

“…Recent findings have emphasized the need to conduct research on cross matching methods [2], [3], [5]. However, this problem received the attention of few researchers.…”

Multiresolution synthetic fingerprint generation

“…Ideally, all these biometric recognition methods must work in an interoperable and ubiquitous way in a highly connected world. Over the years, several studies have tackled the problem of interoperability in fingerprint sensors [13,34,35], evidencing several challenges. Another challenge encountered occurs when matching databases from different countries or regions that use different standards.…”

Image Matching Algorithm Based on Topology Consistency of Bidirectional Optimal Matching Point Pairs

“…Usually, the above fingerprints do not contain enough details, and it is difficult for the conventional matching methods based on the details to achieve ideal results. Although most electronic consumer terminals cannot provide enough computing power for AI-based image matching algorithms, (1)(2)(3)(4)(5)(6) image matching based on local feature points is widely used in the above scenes, such as the well-known scale-invariant feature transform (SIFT) algorithm, (7) the speeded-up robust features (SURF) algorithm, (8) binary robust independent elementary features (BRIEF) algorithm, (9) and the features from accelerated segment test (FAST) and rotated BRIEF (ORB) algorithm. (10) However, differences between the various image matching algorithms based on feature points, such as orientation, coordinates, and sub-vector description, may lead to diverse effects on the same image.…”