Fingerprint Template Ageing Revisited - It’s the Quality, Stupid!

Kirchgasser, Simon; Uhl, Andreas; Castillo-Rosado, Katy; Estevez-Bresó, David; Rodriguez-Hernandez, Emilio; Hernández-Palancar, José

doi:10.1109/btas.2018.8698566

Cited by 4 publications

(8 citation statements)

References 13 publications

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“…However, it is known from previous studies, e.g. [25], that the collected samples are of low quality as several variations like ghost FPs are present in the data. Hence, a major objective for the PLUS MSL FP dataset was to assure that all FP samples are of sufficient FP quality.…”

Section: Inter-session / Inter-sensor Based Studiesmentioning

confidence: 99%

“…The aspect of FP quality was subject of a series of publications done on the CASIA Fingerprint Subject Ageing Version 1.0 dataset [7] which contains FP samples that have been acquired over a time interval of four years. In particular, [25], [28] addressed the influence of quality using various quality metrics, while [26] and [29] focused on a biometric menagerie analysis and the application of non-minutiae based recognition systems.…”

Section: Performance Degradations Due To Quality Issuesmentioning

confidence: 99%

“…Also the analysis focusing on the biometric menagerie [26] introduced as 'Doddington's Zoo' in [9] did not result in a clear statement. The introduction of NFIQ 2.0 as well as an improvement in commercial recognition software allowed to prove that quality is indeed the reason for the observed genuine score reduction which caused the performance degradation [25]. In [63], using a large forensic FP dataset exhibiting a time interval of eight years (cf.…”

Section: Performance Degradations Due To Quality Issuesmentioning

confidence: 99%

“…Furthermore, it is known from previous studies (e.g. [25]) that low quality of FP samples highly influences longitudinal effects' investigations. As with every data acquisition, errors and mistakes during the collection process can occur.…”

Section: Dataset Curationmentioning

confidence: 99%

See 3 more Smart Citations

The PLUS Multi-Sensor and Longitudinal Fingerprint Dataset: An Initial Quality and Performance Evaluation

Kirchgasser

Kauba

Uhl

2022

IEEE Trans. Biom. Behav. Identity Sci.

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In order to assess longitudinal effects in fingerprint biometrics several studies have been carried out in the last decade. Almost all of these investigations focused on non-public forensic fingerprint datasets because there is hardly any publicly available fingerprint database allowing experiments and a corresponding analysis of longitudinal aspects is often infeasible as the contained fingerprint samples are either captured in one session only or with a short time interval of only a few weeks between the sessions. With this work a new fingerprint dataset (108,106 samples) is introduced which can be used for inter-session (longitudinal) as well as inter-sensor fingerprint investigations. A total of ten different capturing devices based on distinct sensing technologies (optical, capacative, thermal, multispectral) is utilized and the imprints of all ten fingers of each of the 50 participating volunteers were acquired at four different time-separated sessions over 2 years. This enables inter-session as well as cross-device evaluations. Additionally, a first quality and recognition performance evaluation deploying several state-of-the-art minutiae based fingerprint recognition schemes and NFIQ 2.0 as quality measure is presented.

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Section: Inter-session / Inter-sensor Based Studiesmentioning

confidence: 99%

Section: Performance Degradations Due To Quality Issuesmentioning

confidence: 99%

Section: Performance Degradations Due To Quality Issuesmentioning

confidence: 99%

Section: Dataset Curationmentioning

confidence: 99%

See 2 more Smart Citations

The PLUS Multi-Sensor and Longitudinal Fingerprint Dataset: An Initial Quality and Performance Evaluation

Kirchgasser

Kauba

Uhl

2022

IEEE Trans. Biom. Behav. Identity Sci.

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“…There are many reports which assess either template aging or permanence that operate at the level of a complete biometric system rather than at the level of single features [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ] which is our interest here. For example, one recent paper [ 19 ] proposes a new method for measuring biometric permanence.…”

Section: Prior Workmentioning

confidence: 99%

Why Temporal Persistence of Biometric Features, as Assessed by the Intraclass Correlation Coefficient, Is So Valuable for Classification Performance

Friedman

Price

Komogortsev

2020

Sensors

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It is generally accepted that relatively more permanent (i.e., more temporally persistent) traits are more valuable for biometric performance than less permanent traits. Although this finding is intuitive, there is no current work identifying exactly where in the biometric analysis temporal persistence makes a difference. In this paper, we answer this question. In a recent report, we introduced the intraclass correlation coefficient (ICC) as an index of temporal persistence for such features. Here, we present a novel approach using synthetic features to study which aspects of a biometric identification study are influenced by the temporal persistence of features. What we show is that using more temporally persistent features produces effects on the similarity score distributions that explain why this quality is so key to biometric performance. The results identified with the synthetic data are largely reinforced by an analysis of two datasets, one based on eye-movements and one based on gait. There was one difference between the synthetic and real data, related to the intercorrelation of features in real data. Removing these intercorrelations for real datasets with a decorrelation step produced results which were very similar to that obtained with synthetic features.

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Extension and Further Evaluation of the PLUS Multi-Sensor and Longitudinal Fingerprint Dataset

Kauba,

Kirchgasser,

Uhl

2024

2024 12th International Workshop on Biometrics and Forensics (IWBF)

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Fingerprint Template Ageing Revisited - It’s the Quality, Stupid!

Cited by 4 publications

References 13 publications

The PLUS Multi-Sensor and Longitudinal Fingerprint Dataset: An Initial Quality and Performance Evaluation

The PLUS Multi-Sensor and Longitudinal Fingerprint Dataset: An Initial Quality and Performance Evaluation

Why Temporal Persistence of Biometric Features, as Assessed by the Intraclass Correlation Coefficient, Is So Valuable for Classification Performance

Extension and Further Evaluation of the PLUS Multi-Sensor and Longitudinal Fingerprint Dataset

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