A Survey on Synthetic Biometrics: Fingerprint, Face, Iris and Vascular Patterns

Makrushin, Andrey; Uhl, Andreas; Dittmann, Jana

doi:10.1109/access.2023.3250852

Cited by 7 publications

(4 citation statements)

References 100 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies indicate that the research focused on generating synthetic iris data is not as extensively explored as in other biometric modalities [1,11]. One of the earliest work in this field was conducted by Cui et al, using principal component analysis (PCA) for iris recognition and the super-resolution method for the synthesis.…”

Section: State Of the Art In Synthetic Iris Data Generationmentioning

confidence: 99%

See 1 more Smart Citation

Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation

Kordas,

Bartuzi-Trokielewicz,

Ołowski

et al. 2024

Sensors

View full text Add to dashboard Cite

In recent years, the advancement of generative techniques, particularly generative adversarial networks (GANs), has opened new possibilities for generating synthetic biometric data from different modalities, including—among others—images of irises, fingerprints, or faces in different representations. This study presents the process of generating synthetic images of human irises, using the recent StyleGAN3 model. The novelty presented in this work consists in producing generated content in both Cartesian and polar coordinate representations, typically used in iris recognition pipelines, such as the foundational work proposed by John Daugman, but hitherto not used in generative AI experiments. The main objective of this study was to conduct a qualitative analysis of the synthetic samples and evaluate the iris texture density and suitability for meaningful feature extraction. During this study, a total of 1327 unique irises were generated, and experimental results carried out using the well-known OSIRIS open-source iris recognition software and the equivalent software, wordlcoin-openiris, newly published at the end of 2023 to prove that (1) no “identity leak” from the training set was observed, and (2) the generated irises had enough unique textural information to be successfully differentiated between both themselves and between them and real, authentic iris samples. The results of our research demonstrate the promising potential of synthetic iris data generation as a valuable tool for augmenting training datasets and improving the overall performance of iris recognition systems. By exploring the synthetic data in both Cartesian and polar representations, we aim to understand the benefits and limitations of each approach and their implications for biometric applications. The findings suggest that synthetic iris data can significantly contribute to the advancement of iris recognition technology, enhancing its accuracy and robustness in real-world scenarios by greatly augmenting the possibilities to gather large and diversified training datasets.

show abstract

Section: State Of the Art In Synthetic Iris Data Generationmentioning

confidence: 99%

“…For instance, the General Data Protection Regulation (GDPR) in the EU has imposed restrictions. These regulations have led to either restricting or banning the collection of biometric datasets entirely [1], creating challenges for researchers and developers who rely on such data for their projects.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation

Kordas,

Bartuzi-Trokielewicz,

Ołowski

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…A notable direction in recent research is the attribute disentanglement technique, like what's seen in the DR-GAN framework [23], which can improve face recognition results. In parallel, there's a growing interest in using synthetic data to further push the boundaries of face recognition, both in terms of evaluating models and creating novel datasets [24], [25]. Our research builds on these advancements.…”

Section: Face Recognition and Synthetic Datamentioning

confidence: 99%

SegTex: A Large Scale Synthetic Face Dataset for Face Recognition

Ambardi,

Hong,

Park

2023

IEEE Access

View full text Add to dashboard Cite

Face recognition remains challenged by data limitations in both scale and diversity, coupled with the ethical dilemmas of using images without the subjects' consent. To address these issues, this paper presents the SegTex framework, a cutting-edge method for generating synthetic face datasets by converting Segmentation maps into Textured images. Using the CelebAHQ-Mask dataset for segmentation maps and extracting facial features from the CelebAMask-HQ dataset, the SegTex method efficiently creates varied synthetic facial characteristics. This approach not only sidesteps the need for real-world data collection but also offers a rich and diverse dataset, essential for improving face recognition algorithm performance. In our experiments, models trained on the SegTex-generated dataset displayed superior performance metrics when compared to those trained on conventional datasets, underscoring the practical utility of our method. This robust performance, combined with the ethical advantages of synthetic data generation, ensures our approach holds significant importance in the field of face recognition.

show abstract

“…Gender classification from iris has been addressed in several publications [5][6][7][8], with different degrees of success [4]. Studies have revealed that most gender information is not actually contained in the iris, but outside of it [4,[9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Impact of Occlusion Masks on Gender Classification from Iris Texture

Yáñez,

Tapia,

Perez

et al. 2024

IET Biometrics

View full text Add to dashboard Cite

Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible. We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification. Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation.

show abstract

A Survey on Synthetic Biometrics: Fingerprint, Face, Iris and Vascular Patterns

Cited by 7 publications

References 100 publications

Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation

Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation

SegTex: A Large Scale Synthetic Face Dataset for Face Recognition

Impact of Occlusion Masks on Gender Classification from Iris Texture

Contact Info

Product

Resources

About