Contactless fingerprinting is an emerging approach to biometric authentication that allows users to scan their fingerprints without touching a scanner. Due to the limited amount of contactless fingerprint data available and the security risks associated with sharing real individuals' fingerprints, it is valuable to explore methods of generating synthetic data that can be used in place of - or in conjunction with - real data to develop and evaluate contactless fingerprinting systems. In this paper, we present and evaluate synthetic fingerphotos generated using StyleGAN2-ADA and StyleGAN3, existing image generation architectures. We evaluate the realism, privacy preservation, and variety of the synthetic fingerphotos by comparing their biometric feature statistics to those of real fingerphotos, computing match scores between real and synthetic fingerphotos, and computing match scores between different synthetic fingerphotos. This paper provides a quantitative comparison point for future evaluations of synthetic fingerphotos. The evaluation code is made available at https://github.com/cmillerlynch/fingerphoto-gan.
Figures & tables
Figure 1: Synthetic fingerphotos generated using StyleGAN2-ADA (top) and StyleGAN3 (bottom). From left to right, the generated images represent index, middle, ring, and little fingers from left hands.
Figure 2: The image processing pipelines that were compared. enhance-CLAHE was selected to process fingerphotos for compatibility with contact-based evaluation and matching tools.
Figure 3: DET curves achieved by VeriFinger on fingerphotos processed using each image processing pipeline.
Real
StyleGAN2-ADA
StyleGAN3
Mean
SD
Mean
SD
Mean
SD
Mean Minutiae Quality
63.70
4.18
62.27
3.90
61.97
3.96
Ridge Ending Minutiae Count
40.38
10.62
40.94
10.25
38.67
9.96
Bifurcation Minutiae Count
45.71
11.26
49.63
11.75
45.06
10.75
Percentage of Bifurcation Minutiae
53.13%
6.52%
54.82%
5.79%
53.87%
6.27%
NFIQ2 Score [ 26 ]
29.80
21.85
30.28
21.82
27.32
22.21
Table 1: Comparison of biometric features of the real fingerphotos in the training dataset and the synthetic fingerphotos generated by the StyleGAN2-ADA and StyleGAN3 models, as measured by VeriFinger and NFIQ2. The enhance-CLAHE image processing pipeline was applied to all fingerphotos prior to feature extraction.
Figure 4: VeriFinger match score distributions for privacy evaluation. The more similar the real and synthetic distributions, the more distinct the synthetic fingerprint patterns are from the real ones in the training dataset.
Figure 5: VeriFinger match score distributions for variety evaluation. The more similar the real and synthetic distributions, the better the synthetic fingerphotos model the variety of fingerprint patterns in the training dataset.
Figure 6: A visualiztion that compares the false accept rates for synthetic fingerphotos at a given threshold to the expected false accept rate for real fingerphotos. The closer each curve is to the diagonal line, the better the corresponding model achieves the corresponding goal. This visualization method provides more information about matching results and the tails of the distributions than histograms.
Contactless fingerprint recognition has gained increasing attention due to its advantages in hygiene and acquisition flexibility. However, the absence of physical contact constraints introduces severe nonlinear geometric distortions caused by free finger poses in 3D space, resulting in a substantial cross-modal domain gap between contactless and conventional contact-based fingerprints. Existing solutions largely rely on explicit geometric correction or image enhancement, which are fragile under extreme pose variations. In this paper, we propose Identity-Consistent Multi-Pose Generation of Contactless Fingerprints (IMPOSE), a physics-inspired framework that synthesizes identity-preserving, multi-pose contactless fingerprint samples to empower recognition models. IMPOSE consists of three stages: (1) rolled fingerprint identity generation via latent diffusion with discrete codebook representations, (2) cross-modal translation from rolled to contactless modality guided by Sauvola-based local adaptive binarization as an identity anchor, and (3) physics-based multi-pose simulation through 3D finger model texture mapping and projection. The generated samples maintain strict identity consistency at the ridge topology level and spatial alignment with standard fingerprint coordinate space. Extensive experiments on the UWA and PolyU CL2CB databases demonstrate that fine-tuning fixed-length dense descriptors (FDD) with IMPOSE-synthesized data achieves state-of-the-art cross-modal matching, reducing EER to 8.74% on UWA and 2.26% on PolyU CL2CB. Synthetic data also yields consistent gains across mainstream representations including DeepPrint and AFRNet, and the hybrid strategy combining synthetic and real data achieves the best overall results. The code and generated samples are available at https://github.com/Yu-Yy/IMPOSE.
Zhiyu Pan, Xiongjun Guan, Jianjiang Feng +1
Department of Automation, Tsinghua University, Beijing 100084, China
The quality and realism of synthetically generated fingerprint images have increased significantly over the past decade fueled by advancements in generative artificial intelligence (GenAI). This has exacerbated the vulnerability of fingerprint recognition systems to data injection attacks, where synthetic fingerprints are maliciously inserted during enrollment or authentication. Hence, there is an urgent need for methods to detect if a fingerprint image is real or synthetic. While it is straightforward to train deep neural network (DNN) models to classify images as real or synthetic, often such DNN models overfit the training data and fail to generalize well when applied to synthetic fingerprints generated using unseen GenAI models. In this work, we formulate synthetic fingerprint detection as a continual few-shot adaptation problem, where the objective is to rapidly evolve a base detector to identify new types of synthetic data. To enable continual few-shot adaptation, we employ a combination of binary cross-entropy and supervised contrastive (applied to the feature representation) losses and replay a few samples from previously known styles during fine-tuning to mitigate catastrophic forgetting. Experiments based on several DNN backbones (as feature extractors) and a variety of real and synthetic fingerprint datasets indicate that the proposed approach achieves a good trade-off between fast adaptation for detecting unseen synthetic styles and forgetting of known styles.
Joseph Geo Benjamin, Anil K. Jain, Karthik Nandakumar
Contactless fingerprint recognition enables hygienic and convenient biometric authentication but poses new challenges for spoof detection due to the absence of physical contact and traditional liveness cues. Most existing methods rely on single-image acquisition and appearance-based features, which often generalize poorly across devices, capture conditions, and spoof materials. In this work, we study paired flash-non-flash contactless fingerprint acquisition as a lightweight active sensing mechanism for spoof detection. Through a preliminary empirical analysis, we show that flash illumination accentuates material- and structure-dependent properties, including ridge visibility, subsurface scattering, micro-geometry, and surface oils, while non-flash images provide a baseline appearance context. We analyze lighting-induced differences using interpretable metrics such as inter-channel correlation, specular reflection characteristics, texture realism, and differential imaging. These complementary features help discriminate genuine fingerprints from printed, digital, and molded presentation attacks. We further examine the limitations of paired acquisition, including sensitivity to imaging settings, dataset scale, and emerging high-fidelity spoofs. Our findings demonstrate the potential of illumination-aware analysis to improve robustness and interpretability in contactless fingerprint presentation attack detection, motivating future work on paired acquisition and physics-informed feature design. Code is available in the repository.