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

This is "The Multimedia Signal Processing and Security Lab", short WaveLab, website. We are a research group at the Computer Sciences Department of the University of Salzburg headed by Andreas Uhl. The short name "WaveLab" already indicates that wavelets are among our favorite tools - we have 15 years of experience in this area. Our research is focused on Multimedia Security including Watermarking, Image and Video Compression, Medical Image Classification, and Biometrics.

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

In this IEEE Transactions on Biometrics, Behavior, and Identity Science paper "The PLUS Multi-Sensor and Longitudinal Fingerprint Dataset: An Initial Quality and Performance Evaluation" we present a comprehensive fingerprint dataset, which provides a publicly available baseline for further investigations on the aspects of FP aging. In addition to the introduction of the dataset, we also aim at presenting an initial analysis regarding quality and recognition performance under the aspect of longitudinal changes using stateof- the-art quality measures and recognition systems. The obtained recognition results can be downloaded from this site to comply with the principles of reproducible research.


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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Data Set

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Result Files and Settings

Here it is possible to download all recognition results obtained using NIST Biometric Image Software (NBIS), ANSI/ISO SDK developed by Innovatrics and VeriFinger SDK 11.0 developed by Neurotechnology. The EER values are summarised in three Excel files for better readability. Each files contains 19 different sheets representing the evaluation categories considered in the corresponding publication.
Furthermore, the obtained quality values for all 12 considered quality metrics are included in the downloadable .zip file as well.

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