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On the Role of Synthetic Data in Biometrics
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On the Role of Synthetic Data in Biometrics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 11090

Special Issue Editors

Dr. Andrey Makrushin

E-Mail Website
Guest Editor
Research Group Multimedia and Security, Department of Computer Science, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
Interests: biometrics; pattern recognition; image processing
Prof. Dr. Jana Dittmann

E-Mail Website
Guest Editor
Research Group Multimedia and Security, Department of Computer Science, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
Interests: forensics; biometrics; watermarking; steganography; security protocols; security evaluations; human interaction within the security context

Special Issue Information

Dear Colleagues,

Recent cross-border regulations on security of private data (e.g., EU GDPR) have made it harder for both industry and academia to use real biometric data for the development of biometric systems. This is how synthetic data come into play. In fact, synthetic data are not linked to any natural person and are therefore not subject to regulations. Data privacy is not the only reason that using synthetic data may be beneficial. From a practical perspective, generating a large amount of random biometric samples is more cost efficient than acquiring biometric samples from people. Moreover, with synthetic samples, it is easier to control the equal distribution of attributes such as gender, race, or age in a dataset to ensure fair and unbiased application of machine learning.

We invite papers introducing recent advances in generating all kinds of synthetic biometric data. We especially welcome studies concerned with quality assessment of synthetic samples, including the privacy aspect. Last but not least, we encourage the submission of papers introducing publicly available datasets of synthetic biometric samples.

Dr. Andrey Makrushin
Prof. Dr. Jana Dittmann
Guest Editors

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Published Papers (4 papers)

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25 pages, 3754 KiB  
Article
Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms
by Andrey Makrushin, Venkata Srinath Mannam and Jana Dittmann
Appl. Sci. 2023, 13(18), 10000; https://doi.org/10.3390/app131810000 - 5 Sep 2023
Cited by 3 | Viewed by 2046
Abstract
The datasets of synthetic biometric samples are created having in mind two major objectives: bypassing privacy concerns and compensating for missing sample variability in datasets of real biometric samples. If the purpose of generating samples is the evaluation of biometric systems, the foremost [...] Read more.
The datasets of synthetic biometric samples are created having in mind two major objectives: bypassing privacy concerns and compensating for missing sample variability in datasets of real biometric samples. If the purpose of generating samples is the evaluation of biometric systems, the foremost challenge is to generate so-called mated impressions—different fingerprints of the same finger. Note that for fingerprints, the finger’s identity is given by the co-location of minutiae points. The other challenge is to ensure the realism of generated samples. We solve both challenges by reconstructing fingerprints from pseudo-random minutiae making use of the pix2pix network. For controlling the identity of mated impressions, we derive the locations and orientations of minutiae from randomly created non-realistic synthetic fingerprints and slightly modify them in an identity-preserving way. Our previously trained pix2pix models reconstruct fingerprint images from minutiae maps, ensuring that the realistic appearance is transferred from training to synthetic samples. The main contribution of this work lies in creating and making public two synthetic fingerprint datasets of 500 virtual subjects with 8 fingers each and 10 impressions per finger, totaling 40,000 samples in each dataset. Our synthetic datasets are designed to possess characteristics of real biometric datasets. Thus, we believe they can be applied for the privacy-friendly testing of fingerprint recognition systems. In our evaluation, we use NFIQ2 for approving the visual quality and Verifinger SDK for measuring the reconstruction success. Full article
(This article belongs to the Special Issue On the Role of Synthetic Data in Biometrics)
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22 pages, 53820 KiB  
Article
Identifying Synthetic Faces through GAN Inversion and Biometric Traits Analysis
by Cecilia Pasquini, Francesco Laiti, Davide Lobba, Giovanni Ambrosi, Giulia Boato and Francesco De Natale
Appl. Sci. 2023, 13(2), 816; https://doi.org/10.3390/app13020816 - 6 Jan 2023
Cited by 6 | Viewed by 2629
Abstract
In the field of image forensics, notable attention has been recently paid toward the detection of synthetic contents created through Generative Adversarial Networks (GANs), especially face images. This work explores a classification methodology inspired by the inner architecture of typical GANs, where vectors [...] Read more.
In the field of image forensics, notable attention has been recently paid toward the detection of synthetic contents created through Generative Adversarial Networks (GANs), especially face images. This work explores a classification methodology inspired by the inner architecture of typical GANs, where vectors in a low-dimensional latent space are transformed by the generator into meaningful high-dimensional images. In particular, the proposed detector exploits the inversion of the GAN synthesis process: given a face image under investigation, we identify the point in the GAN latent space which more closely reconstructs it; we project the vector back into the image space, and we compare the resulting image with the actual one. Through experimental tests on widely known datasets (including FFHQ, CelebA, LFW, and Caltech), we demonstrate that real faces can be accurately discriminated from GAN-generated ones by properly capturing the facial traits through different feature representations. In particular, features based on facial landmarks fed to a Support Vector Machine consistently yield a global accuracy of above 88% for each dataset. Furthermore, we experimentally prove that the proposed detector is robust concerning routinely applied post-processing operations. Full article
(This article belongs to the Special Issue On the Role of Synthetic Data in Biometrics)
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19 pages, 5401 KiB  
Article
Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition
by Mathias Ibsen, Christian Rathgeb, Pawel Drozdowski and Christoph Busch
Appl. Sci. 2022, 12(24), 12969; https://doi.org/10.3390/app122412969 - 16 Dec 2022
Cited by 4 | Viewed by 3553
Abstract
Systems that analyse faces have seen significant improvements in recent years and are today used in numerous application scenarios. However, these systems have been found to be negatively affected by facial alterations such as tattoos. To better understand and mitigate the effect of [...] Read more.
Systems that analyse faces have seen significant improvements in recent years and are today used in numerous application scenarios. However, these systems have been found to be negatively affected by facial alterations such as tattoos. To better understand and mitigate the effect of facial tattoos in facial analysis systems, large datasets of images of individuals with and without tattoos are needed. To this end, we propose a generator for automatically adding realistic tattoos to facial images. Moreover, we demonstrate the feasibility of the generation by using a deep learning-based model for removing tattoos from face images. The experimental results show that it is possible to remove facial tattoos from real images without degrading the quality of the image. Additionally, we show that it is possible to improve face recognition accuracy by using the proposed deep learning-based tattoo removal before extracting and comparing facial features. Full article
(This article belongs to the Special Issue On the Role of Synthetic Data in Biometrics)
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18 pages, 4844 KiB  
Article
Biometric Performance as a Function of Gallery Size
by Lee Friedman, Hal Stern, Vladyslav Prokopenko, Shagen Djanian, Henry Griffith and Oleg Komogortsev
Appl. Sci. 2022, 12(21), 11144; https://doi.org/10.3390/app122111144 - 3 Nov 2022
Cited by 3 | Viewed by 1764
Abstract
Many developers of biometric systems start with modest samples before general deployment. However, they are interested in how their systems will work with much larger samples. To assist them, we evaluated the effect of gallery size on biometric performance. Identification rates describe the [...] Read more.
Many developers of biometric systems start with modest samples before general deployment. However, they are interested in how their systems will work with much larger samples. To assist them, we evaluated the effect of gallery size on biometric performance. Identification rates describe the performance of biometric identification, whereas ROC-based measures describe the performance of biometric authentication (verification). Therefore, we examined how increases in gallery size affected identification rates (i.e., Rank-1 Identification Rate, or Rank-1 IR) and ROC-based measures such as equal error rate (EER). We studied these phenomena with synthetic data as well as real data from a face recognition study. It is well known that the Rank-1 IR declines with increasing gallery size, and that the relationship is linear against log(gallery size). We have confirmed this with synthetic and real data. We have shown that this decline can be counteracted with the inclusion of additional information (features) for larger gallery sizes. We have also described the curves which can be used to predict how much additional information would be required to stabilize the Rank-1 IR as a function of gallery size. These equations are also linear in log(gallery size). We have also shown that the entire ROC-curve was not systematically affected by gallery size, and so ROC-based scalar performance metrics such as EER are also stable across gallery size. Unsurprisingly, as additional uncorrelated features are added to the model, EER decreases. We were interested in determining the impact of adding more features on the median, spread and shape of similarity score distributions. We present evidence that these decreases in EER are driven primarily by decreases in the spread of the impostor similarity score distribution. Full article
(This article belongs to the Special Issue On the Role of Synthetic Data in Biometrics)
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