Smudge Noise for Quality Estimation of Fingerprints and its Validation

Richter, Robin; Gottschlich, Carsten; Mentch, Lucas; Thai, Duy Hoang; Huckemann, Stephan

doi:10.1109/tifs.2018.2889258

Cited by 9 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The magnitude of local sensitivity in the process of protecting fingerprint images through dynamic privacy budget corresponds to the density of the distribution of matching feature points, thereby enhancing the rationality of calculating local sensitivity when introducing perturbation noise to fingerprint images using Laplace mechanism. Definition 4 and (4) (5) demonstrate that the exponential mechanism computes the allocated privacy budget based on the calculation of local sensitivity for extracting local images.…”

Section: Dynamic Budget Fingerprint Image Non-global Protection Methodsmentioning

confidence: 99%

“…Definition 4: Local sensitivity: The local sensitivity of query function Q , denoted as ( 4) and (5), can be expressed when Q maps from ( )…”

Section: A Differential Privacymentioning

confidence: 99%

“…The protection process of fingerprint images is closely intertwined with the recognition process, wherein the matching method employed in fingerprint image recognition serves as a pivotal step in fingerprint recognition technology. The acquisition procedure of the fingerprint acquisition device projects three-dimensional fingerprints onto a two-dimensional plane [4], preprocesses the two-dimensional fingerprint image to enhance its quality [5], and subsequently extracts the unique features embedded within it. Directly publishing unprotected fingerprint images is highly likely to result in significant breaches of personal privacy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Research on Local Fingerprint Image Differential Privacy Protection Method Based on Clustering Algorithm and Regression Algorithm Segmentation Image

Liu,

Zhi,

Zhao

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Fingerprint recognition technology has been extensively employed across various sectors of society. The direct publication of fingerprint images leads to the disclosure of sensitive information. According to the fingerprint image identification process, the fingerprint image protection process is actually the fingerprint image feature point location information, quantity information, type information protection. To address this issue, this paper proposes a machine learning and differential privacy-based fingerprint image publish algorithm called DP-RKLAP. The algorithm establishes a protected process to match feature points in fingerprint images and employs a clustering algorithm for initial segmentation of the images (KLAP). Additionally, a multinomial regression algorithm is applied to preprocess the segmented image regions, constructing a regression model that accurately determines fluctuation amplitudes for precise segmentation of protected areas containing matching feature points (RKLAP). Considering the uncertainty in segmentation caused by uncertain feature point locations in fingerprint images, we introduce a dynamic allocation method (DP) for privacy budget allocation. The exponential mechanism leverages the relationship between the number of matching feature points and segmentation regions to dynamically allocate privacy budgets within the Laplace mechanism framework of differential privacy technique, thereby achieving local protection publish for fingerprint images. This reduction in sensitivity effectively mitigates noise errors during the process of privacy protection, thereby achieving a balance between privacy and usability of the fingerprint images. Experimental results confirm that our proposed method successfully achieves privacy protection during the publishing process of fingerprint images, while still maintaining high usability after protected publishing and matching verification using real-world datasets.

show abstract

Section: Dynamic Budget Fingerprint Image Non-global Protection Methodsmentioning

confidence: 99%

“…Definition 4: Local sensitivity: The local sensitivity of query function Q , denoted as ( 4) and (5), can be expressed when Q maps from ( )…”

Section: A Differential Privacymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on Local Fingerprint Image Differential Privacy Protection Method Based on Clustering Algorithm and Regression Algorithm Segmentation Image

Liu,

Zhi,

Zhao

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Feature-or minutiaespecific metrics provide individual scores for certain aspects of each print. For example, a quality metric developed by Peskin et al [19] assesses the gradient of contrast intensity around a particular feature, and the smudge noise quality estimator metric (SNoQE) [21] assesses the relative noise (i.e., smudge vs. dryness) associated with print features. Kellman et al [12] also developed several quantitative measures of prints (e.g., total area, block contrast) related to print quality.…”

Section: Print Quality Metricsmentioning

confidence: 99%

Latent print quality in blind proficiency testing: Using quality metrics to examine laboratory performance

Gardner

Neuman

Kelley

2021

Forensic Science International

View full text Add to dashboard Cite

Calls for blind proficiency testing in forensic science disciplines intensified following the 2009 National Academy of Sciences report and were echoed in the 2016 report by the President's Council of Advisors on Science and Technology. Both practitioners and scholars have noted that "open" proficiency tests, in which analysts know they are being tested, allow for test-taking behavior that is not representative of behavior in routine casework. This study reports the outcomes of one laboratory's blind quality control (BQC) program. Specifically, we describe results from approximately 2.5 years of blind cases in the latent print section (N = 376 latent prints submitted as part of 144 cases). We also used a widely available quality metrics software (LQMetrics) to explore relationships between objective print quality and case outcomes. Results revealed that nearly all BQC prints (92.0%) were of sufficient quality to enter into AFIS. When prints had a source present in AFIS, 41.7% of print searches resulted in a candidate list containing the true source. Examiners committed no false positive errors but other types of errors were more common. Average print quality was in the midpoint of the range (53.4 on a 0-to-100 scale), though prints were evenly distributed across the Good, Bad, and Ugly categories. Quality metrics were significantly associated with sufficiency determinations, examiner conclusions, and examiner accuracy. Implications for blind testing and the use of quality metrics in routine casework as well as proficiency testing are discussed.

show abstract

“…In contrast to minutia quality assessment, more research was done on the quality assessment on full fingerprints. Traditional approaches are based on the use of local [32,37,3,45,31] and global hand-crafted [22,7,27,11,9,28] features, or approaches that combine both kinds of features [1,44,29,8]. Besides the use of hand-crafted features, deep feature-learning solutions [41,26,13] were proposed recently.…”

Section: Fingerprint Quality Assessmentmentioning

confidence: 99%

MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence

Terhörst¹,

Boller²,

Damer³

et al. 2021

Preprint

View full text Add to dashboard Cite

An essential factor to achieve high accuracies in fingerprint recognition systems is the quality of its samples. Previous works mainly proposed supervised solutions based on image properties that neglects the minutiae extraction process, despite that most fingerprint recognition techniques are based on detected minutiae. Consequently, a fingerprint image might be assigned a high quality even if the utilized minutia extractor produces unreliable information. In this work, we propose a novel concept of assessing minutia and fingerprint quality based on minutia detection confidence (MiDeCon). MiDeCon can be applied to an arbitrary deep learning based minutia extractor and does not require quality labels for learning. We propose using the detection reliability of the extracted minutia as its quality indicator. By combining the highest minutia qualities, MiDeCon also accurately determines the quality of a full fingerprint. Experiments are conducted on the publicly available databases of the FVC 2006 and compared against several baselines, such as NIST's widely-used fingerprint image quality software NFIQ1 and NFIQ2. The results demonstrate a significantly stronger quality assessment performance of the proposed MiDeCon-qualities as related works on both, minutia-and fingerprint-level. The implementation is publicly available.

show abstract

Smudge Noise for Quality Estimation of Fingerprints and its Validation

Cited by 9 publications

References 29 publications

Research on Local Fingerprint Image Differential Privacy Protection Method Based on Clustering Algorithm and Regression Algorithm Segmentation Image

Research on Local Fingerprint Image Differential Privacy Protection Method Based on Clustering Algorithm and Regression Algorithm Segmentation Image

Latent print quality in blind proficiency testing: Using quality metrics to examine laboratory performance

MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence

Contact Info

Product

Resources

About