Enhancement-Driven Pretraining for Robust Fingerprint Representation

Learning

Ekta Gavas

1 a

, Kaustubh Olpadkar

2 b

and Anoop Namboodiri

1 c

Centre for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

Stony Brook University, U.S.A.

Keywords:

Fingerprint Representation Learning, Fingerprint Veriﬁcation, Self-Supervised Learning, Deep Learning.

Abstract:

Fingerprint recognition stands as a pivotal component of biometric technology, with diverse applications from

identity veriﬁcation to advanced search tools. In this paper, we propose a unique method for deriving robust

ﬁngerprint representations by leveraging enhancement-based pre-training. Building on the achievements of U-

Net-based ﬁngerprint enhancement, our method employs a specialized encoder to derive representations from

ﬁngerprint images in a self-supervised manner. We further reﬁne these representations, aiming to enhance the

veriﬁcation capabilities. Our experimental results, tested on publicly available ﬁngerprint datasets, reveal a

marked improvement in veriﬁcation performance against established self-supervised training techniques. Our

ﬁndings not only highlight the effectiveness of our method but also pave the way for potential advancements.

Crucially, our research indicates that it is feasible to extract meaningful ﬁngerprint representations from de-

graded images without relying on enhanced samples.

1 INTRODUCTION

Fingerprint recognition remains a cornerstone in bio-

metric identiﬁcation, valued for its uniqueness, per-

manence, and user-friendliness (Maltoni et al., 2022;

Wayman et al., 2005; Allen et al., 2005). As demand

in law enforcement, personal identiﬁcation, and se-

cure authentication continues to rise, the need to en-

hance precision and efﬁciency in ﬁngerprint recogni-

tion systems becomes increasingly vital (Allen et al.,

2005).

Despite advancements in the ﬁeld, challenges per-

sist, including handling partial or distorted ﬁnger-

prints, managing high interclass similarity, and ad-

dressing the expansive dimensionality of the feature

space (Maltoni et al., 2022; Hong et al., 1998; Cap-

pelli et al., 2007). Many state-of-the-art works in ﬁn-

gerprint matching rely on minutia-based approaches

(Ratha et al., 1996; Chang et al., 1997; Maltoni et al.,

2022; Cappelli et al., 2010b; Cappelli et al., 2010a;

Jain et al., 2001; Jain et al., 1997). This involves

extracting minutiae and matching templates to deter-

mine similarity, but traditional minutia-based meth-

https://orcid.org/0000-0001-6437-3357

https://orcid.org/0009-0008-3811-4771

https://orcid.org/0000-0002-4638-0833

ods face limitations like noise sensitivity and difﬁ-

culty with partial prints (Maltoni et al., 2009; Hong

et al., 1998; Maltoni et al., 2009; Zaeri, 2011).

In contrast, Convolutional Neural Networks

(CNNs) present a contemporary solution, effec-

tively overcoming limitations and improving accu-

racy. CNNs handle partial prints, tolerate distortions,

and adapt to diverse ﬁnger conditions, showcasing

scalability and efﬁcient comparison even with grow-

ing databases (Nguyen et al., 2018; Deshpande et al.,

2020; Darlow and Rosman, 2017; Tang et al., 2017;

Engelsma et al., 2019).

The surge in self-supervised learning techniques

in machine learning has extended to ﬁngerprint bio-

metrics (Jaiswal et al., 2020; Liu et al., 2021; Jing

and Tian, 2020). Offering solutions to challenges

in data acquisition, self-supervised learning bypasses

time-consuming labeling processes. In this paper,

we explore the potential of deep CNNs for supe-

rior matching performance, proposing a pretraining

technique based on U-Net for ﬁngerprint enhance-

ment. The U-Net model, known for biomedical im-

age segmentation (Ronneberger et al., 2015), effec-

tively enhances ﬁngerprints by extracting contextual

information, aiming to derive compact, discrimina-

tive ﬁngerprint embeddings. Our study pursues two

objectives: proposing a pretraining technique with

Gavas, E., Olpadkar, K. and Namboodiri, A.

Enhancement-Driven Pretraining for Robust Finger print Representation Learning.

DOI: 10.5220/0012474900003660

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024) - Volume 2: VISAPP, pages

821-828

ISBN: 978-989-758-679-8; ISSN: 2184-4321

821

U-Net and assessing the efﬁcacy of these represen-

tations through veriﬁcation performance against ex-

isting self-supervised methods. We experiment with

training and inference techniques to optimize the use

of representations for ﬁngerprint veriﬁcation tasks.

This paper aims to deepen our understanding of ﬁn-

gerprint recognition, inspiring future progress in this

direction.

1.1 Contributions

Here are the main contributions of this work:

1. We suggest a pre-training technique with ﬁn-

gerprint enhancement task on our encoder and

demonstrate the usefulness of this approach in

representation learning in self-supervised setting.

2. We describe a method to ﬁne-tune the learned em-

beddings for ﬁngerprint veriﬁcation task.

3. We evaluate our approach with various evaluation

metrics demonstrating its effectiveness in ﬁnger-

print veriﬁcation task and also provide a compar-

ison with previous state-of-the-art self-supervised

learning methods.

1.2 Related Work

The need for improved ﬁngerprint recognition tools

has spurred the development of effective ﬁngerprint

representation methods. Various approaches, draw-

ing on domain knowledge, have enhanced the accu-

racy and speed of ﬁngerprint identiﬁcation (Engelsma

et al., 2019; Tang et al., 2017). This paper explores

a pretraining technique, focusing on an enhancement

task to optimize model learning for representation.

1.2.1 Image Enhancement

Early ﬁngerprint image enhancement methods, such

as Gabor ﬁlters and Fourier Transform, faced chal-

lenges with poor quality, noise, and pattern varia-

tions (Greenberg et al., 2002; Hong et al., 1998; Kim

et al., 2002; Yang et al., 2002; Liu et al., 2014;

Sherlock et al., 1992; Chikkerur et al., 2005; Rah-

man et al., 2008). Convolutional Neural Networks

(CNNs), adept at hierarchical learning, have proven

effective in capturing minutiae and latent features, en-

hancing recognition accuracy (Nguyen et al., 2018;

Deshpande et al., 2020; Tang et al., 2017). U-Net,

originally designed for biomedical image segmenta-

tion, has been adapted for ﬁngerprint enhancement

(Ronneberger et al., 2015). Various modiﬁcations

to U-Net, tailored for ﬁngerprint enhancement tasks,

have been proposed (Gavas and Namboodiri, 2023;

Qian et al., 2019; Liu and Qian, 2020).

1.2.2 Self-supervised Learning Techniques

Self-supervised learning, an alternative to traditional

supervised learning, capitalizes on unlabeled data us-

ing pretext tasks for feature representation (Jaiswal

et al., 2020; Jing and Tian, 2020). Contrastive learn-

ing, a cornerstone of self-supervised learning, dif-

ferentiates between similar and dissimilar instances

(Liu et al., 2021). Techniques like SimCLR, MoCo,

BYOL, SwAV, and Noise Contrastive Estimation

showcase the diversity of contrastive learning ap-

proaches (Chen et al., 2020a; Chen et al., 2020b;

Grill et al., 2020; Caron et al., 2021; Gutmann and

Hyv

arinen, 2010). These methods provide insights

into contrastive learning’s potential applications in

ﬁngerprint biometrics.

2 METHODOLOGY

The methodology for our research is constructed

around a two-stage framework to probe the potential

of self-supervised learning in ﬁngerprint representa-

tion learning. A broad overview of the process is as

follows:

• Stage 1: Self-Supervised Pre-training: This is

the initial stage of our methodology, in which

we perform pre-training of our models in a self-

supervised manner. It includes the application of

both existing self-supervised learning techniques

as well as our novel enhancement-based approach

for this task. This stage intends to leverage the

power of unlabeled data to learn meaningful rep-

resentations that can serve as a starting point for

subsequent stages. Notably, for all methods, we

keep the encoder architecture the same. While

other self-supervised methods traditionally use

encoders like ResNet or Vision Transformers, in

our framework we use the encoder of our U-Net-

based model to ensure a fair comparison.

• Stage 2: Probing Experiments: Upon comple-

tion of the pre-training phase, we progress to the

second stage where a few linear layers (MLP)

are added on top of the frozen pre-trained en-

coder, making the representations 512-d. We then

perform probing experiments using this newly

formed model. By keeping the encoder part

frozen, we ensure that the model adapts the exist-

ing representations for the veriﬁcation task with-

out altering the learned patterns from the self-

supervised pre-training phase.

Following this framework, we navigate through

the process of adapting and implementing self-

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Figure 1: a) Architecture with veriﬁcation objective i.e with binary classiﬁer (at training and inference) b) Architecture to

compute similarity scores (at inference). The dotted arrows indicate networks having tied weights (siamese network structure).

Figure 2: U-Net architecture for enhancement task for the pre-training stage in the self-supervised setting. For representation

learning, the decoder is discarded and the binary classiﬁer is attached.

supervised learning techniques, exploring a U-Net-

based pre-training strategy, and conducting probing

experiments with pre-trained networks. The sections

below provide a detailed overview of the procedures

involved in each stage.

2.1 U-Net-Based Pretraining

While applying existing self-supervised methods to

ﬁngerprint data offers a valuable starting point, we

advocate for a self-supervised learning method tai-

lored speciﬁcally for the uniqueness of ﬁngerprint

data. Drawing on our insights from U-Net-based en-

hancement works, our approach employs the training

of a ﬁngerprint enhancement model as a form of self-

supervision.

We employ U-Net-based ﬁngerprint enhancement

for pre-training, hypothesizing that the U-Net en-

coder, trained on ﬁngerprint enhancement, holds valu-

able ﬁngerprint representations. Enhancing a ﬁn-

gerprint image becomes an effective self-supervised

task, encouraging the model to learn useful, ﬁnger-

print representations. The pre-trained encoder al-

ready encapsulates crucial information about the ﬁn-

gerprint, providing a foundation for further represen-

tation learning. The quality of these initial repre-

sentations hinges on the efﬁcacy of the U-Net-based

enhancement model, emphasizing the signiﬁcance of

the model’s design and training.

For enhancement-based pre-training, we use the

basic U-Net architecture (Figure 2) to optimize the

ﬁngerprint enhancement task. This simple image-to-

image network takes a degraded ﬁngerprint image as

input, degraded with various noises. The network

aims to predict an enhanced version of the ﬁnger-

print image by removing noise while maintaining and

restoring the ridge structure. This ensures the network

learns minute details of ﬁngerprint structure and en-

hances it where possible, aiding robust feature repre-

sentation extraction later. We term it self-supervision

as we use supervision from the enhancement task in-

directly. This leverages a smaller amount of labeled

Enhancement-Driven Pretraining for Robust Fingerprint Representation Learning

823

data with limited impressions and identities.

Table 1 are the results of the ﬁrst stage of our net-

work where we are pre-training the U-Net model for

enhancement task. Results of this pre-training stage

are demonstrated in Table 1.

2.2 Learning Fingerprint

Representation

After the self-supervised pre-training, we conduct the

probing experiments using the pre-trained networks.

These experiments aim to assess the usefulness of the

learned representations for the task of ﬁngerprint ver-

iﬁcation. For this, we add a 3-layer MLP projection

head on top of the frozen encoder part of the pre-

trained network. We then train this model using a

Sentence-BERT-like (Reimers and Gurevych, 2019)

siamese architecture, with a limited amount of labeled

data for the ﬁngerprint veriﬁcation task. We con-

catenate the ﬁngerprint representations u and v of the

image pair with the element-wise difference |u − v|

and then pass it through the linear layers and train

it for binary-classiﬁcation objective as illustrated in

Figure 1. By keeping the encoder part frozen, the

model learns to adapt the existing representations for

the veriﬁcation task, without changing the underlying

learned patterns. This approach allows us to leverage

a large amount of unlabeled data to learn initial repre-

sentations and a limited amount of labeled data for su-

pervised adaptation. Note that in the supervised ﬁne-

tuning, allowing modiﬁcations in the encoder weights

can lead to higher performance on the end task, which

is the future scope of this work. As our goal here

is to examine the robustness of the learned represen-

tations by different pre-training techniques, we keep

the encoder frozen. In summary, the combination

of self-supervised pre-training with supervised ﬁne-

tuning offers a promising learning framework for ﬁn-

gerprint biometrics. Our methodology aims to lever-

age the strengths of both self-supervised and super-

vised learning, offering a pathway towards robust, ef-

ﬁcient, and data-savvy ﬁngerprint biometrics systems.

3 EXPERIMENTS

In this section, we discuss the experiments performed

to evaluate our proposed approach’s efﬁcacy. We

cover the speciﬁcs of our experimental setup, includ-

ing the datasets used, the training details, and the eval-

uation metrics employed.

3.1 Datasets and Preprocessing

This study employs datasets comprising synthetic and

real-world ﬁngerprint images from SFinGe (Cappelli,

2004), FVC (Maio et al., 2002a; Maio et al., 2002b;

Maio et al., 2004), and NIST SD-302 (Fiumara et al.,

2019). SFinGe simulates real-world challenges, while

FVC and NIST SD-302 offer large-scale, realistic ﬁn-

gerprint data for generalizability. This dataset com-

bination enables model training and evaluation under

diverse conditions. Synthetic data provides scalability

and control, while real-world data ensures applicabil-

ity. During self-supervised pre-training, only train-

ing dataset ﬁngerprint images are used, without la-

bels. Ground truth is needed for the enhancement

task, obtained from clean images for SFinGe and gen-

erated for NIST SD-302 and FVC using a classical ap-

proach (Hong et al., 1998). The next phase involves

a binary classiﬁcation task for ﬁngerprint veriﬁcation.

Data augmentation, vital for self-supervised learning,

employs random transformations like rotation, color

jitter, resize, crop, and Gaussian blur.

3.2 Implementation Details

We perform experiments using the PyTorch (Paszke

et al., 2017) framework on an Nvidia GeForce RTX

2080 Ti GPU for training.

Our proposed enhancement-based pre-training

utilizes the U-Net architecture. This U-Net encoder

is employed consistently for pre-training with other

self-supervised methods to ensure fair comparison.

The U-Net has a depth of 5 layers, each with 2

convolutions, and expects 512 x 512-pixel grayscale

ﬁngerprint images. The encoder outputs a 4096-

dimensional vector bottleneck, reduced to 512-d with

an MLP projection head. Depth-wise convolutions

minimize parameters. We use L

loss for U-Net’s

enhancement-based pre-training, adopting losses de-

scribed in respective papers for other techniques.

For pre-training with existing self-supervised

methods, a grid search identiﬁes optimal hyperparam-

eters. Models are pre-trained for 50 epochs with early

stopping.

In probing experiments, MLP projection head

weights are adapted for veriﬁcation while keeping

encoder weights ﬁxed. We create 1:3 positive-to-

negative pairs for training and testing veriﬁcation sets

from each dataset. After training, models are eval-

uated on test sets, reporting metrics like veriﬁcation

accuracy, precision, recall, and F1-score. Results are

presented in two ways: 1) using the binary classiﬁer

over the MLP projection head (Figure 1-a) and 2) uti-

lizing representations with thresholds on cosine sim-

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Table 1: Enhancement pre-training stage results with U-Net architecture.

Method SSIM RSME PSNR NFIQ2

Raw Images 0.595 113.23 6.53 33.42

Enhancement U-Net 0.903 39.38 16.72 51.26

Figure 3: Degraded (top row) and Enhanced (bottom row) image pairs on FVC dataset from enhancement pre-training.

Table 2: Veriﬁcation accuracy on SFinGe test dataset with genuine and imposter pairs.

SFinGe - Accuracy

Method

Classiﬁcation Similarity

Imposter Genuine Entire Data Imposter Genuine Entire Data

SimCLR 0.968 0.881 0.946 0.982 0.749 0.923

SimSiam 0.972 0.362 0.819 0.888 0.648 0.828

MoCo 0.963 0.881 0.942 0.955 0.845 0.927

BYOL 0.96 0.825 0.926 0.963 0.718 0.901

Ours 0.982 0.886 0.958 0.975 0.847 0.943

Table 3: F1 score on SFinGe test dataset with genuine and imposter pairs.

SFinGe - F1 score

Method

Classiﬁcation Similarity

Imposter Genuine Entire Data Imposter Genuine Entire Data

SimCLR 0.98 0.8 0.803 0.98 0.78 0.781

SimSiam 0.96 0.44 0.442 0.92 0.47 0.469

MoCo 0.98 0.79 0.785 0.97 0.74 0.737

BYOL 0.97 0.74 0.742 0.97 0.69 0.689

Ours 0.99 0.86 0.858 0.98 0.81 0.821

ilarity (Figure 1-b). The ﬁrst method evaluates the

model as an end-to-end veriﬁcation network, while

the second explores the potential of learned represen-

tations for similarity search and recognition tasks.

3.3 Results

The models are ﬁrst pre-trained to learn ﬁnger-

print representations using the enhancement-based

approach and various self-supervised learning strate-

gies. Because these representations are not explic-

itly trained for ﬁngerprint veriﬁcation or identiﬁca-

tion, using them directly for evaluation is inappropri-

ate. To gauge the stability and usefulness of these

learned representations, we add linear layers to the

frozen pre-trained encoders and then train the mod-

els for ﬁngerprint veriﬁcation tasks. The encoders re-

main frozen, allowing only the weights of the MLP

to adjust to the task, keeping the original representa-

tions unchanged. This setup aids in comparing the ef-

ﬁcacy of different self-supervised learning techniques

against our method. The results of our probing exper-

iments are presented in Table 4 (Veriﬁcation Accu-

racy) and 5 (F1-score). The veriﬁcation accuracy and

F1-score on the SFinGe test set are shown in Tables 2

and 3 respectively. Figure 3 shows a few sample pairs

of input and predicted images from the pre-trained U-

Net model on the enhancement task used in our ap-

Enhancement-Driven Pretraining for Robust Fingerprint Representation Learning

825

Table 4: Veriﬁcation accuracy on FVC test dataset with genuine and imposter pairs.

FVC - Accuracy

Method

Classiﬁcation Similarity

Imposter Genuine Entire Data Imposter Genuine Entire Data

SimCLR 0.915 0.619 0.841 0.943 0.537 0.841

SimSiam 0.956 0.122 0.747 0.387 0.733 0.473

MoCo 0.902 0.522 0.807 0.896 0.56 0.812

BYOL 0.886 0.568 0.806 0.926 0.477 0.813

Ours 0.957 0.73 0.900 0.933 0.818 0.904

Table 5: F1 score on FVC test dataset with genuine and imposter pairs.

FVC - F1 score

Method

Classiﬁcation Similarity

Imposter Genuine Entire Data Imposter Genuine Entire Data

SimCLR 0.94 0.5 0.502 0.95 0.51 0.51

SimSiam 0.94 0.16 0.156 0.55 0.19 0.186

MoCo 0.93 0.42 0.417 0.92 0.43 0.431

BYOL 0.92 0.42 0.421 0.94 0.43 0.432

Ours 0.97 0.68 0.679 0.96 0.66 0.659

proach.

Our approach is compared with methods like Sim-

CLR v2, SimSiam, MoCo v2, and BYOL on the

SFinGe and FVC test sets for ﬁngerprint veriﬁcation.

Veriﬁcation accuracy serves as the evaluation metric

for each method. The test data for ﬁngerprint ver-

iﬁcation consists of a 1:3 ratio of positive to nega-

tive pairs, setting the random guess accuracy at 75%.

Veriﬁcation accuracy is measured in two ways as de-

scribed before. This is presented in the below tables

under the ‘Classiﬁer’ column. The second way is rep-

resented under the ‘Similarity’ column in the tables.

Moreover, we also report the ROC curves in Figure 4

for both datasets.

As seen from the results, our enhancement-based

pre-training method consistently outperforms other

self-supervised strategies across both test sets. Sim-

CLRv2 also consistently performs well. SimSiam

and BYOL methods show comparatively poor perfor-

mance. It is noteworthy that all models perform better

on the SFinGe test set than on the FVC test set. We

believe this is due to two primary factors: the train-

ing sets contain more data from SFinGe than FVC,

potentially resulting in a bias towards the former, and

SFinGe is a synthetic dataset while FVC consists of

real ﬁngerprints, making the latter more challenging.

Hence, the performance of models on FVC data is the

real measure of the efﬁcacy of models. Importantly,

our method also provides superior performance when

veriﬁcation is based on the similarity of the represen-

tations, suggesting that the learned representations are

also useful for ﬁngerprint recognition.

4 LIMITATIONS AND FUTURE

WORK

Despite promising results, our model demonstrates

greater efﬁcacy on the synthetic SFinGe dataset than

on the real-world FVC dataset. This could be at-

tributed to potential bias from underrepresentation of

FVC data in training sets and complexities in real-

life ﬁngerprint data. Another limitation is the lack

of speciﬁc training and evaluation for the ﬁngerprint

recognition task. While our model shows potential, a

dedicated evaluation is essential for a comprehensive

understanding of its performance. The effectiveness

of self-supervised learning relies on data quality and

diversity, and our study used linear probing, leaving

room to explore alternative approaches like softmax

or ArcFace-based classiﬁcation.

Future work should address limitations by in-

corporating a more diverse set of real-world ﬁnger-

print datasets during training. Exploring the option

of training the encoder with a smaller learning rate,

rather than freezing it, could enhance generalizabil-

ity and robustness. Speciﬁc training and evaluation

for the recognition task, investigating alternative lin-

ear probing techniques, and exploring various self-

supervised learning methods are valuable directions

for further optimization.

VISAPP 2024 - 19th International Conference on Computer Vision Theory and Applications

826

Figure 4: ROC curve based on similarity scores on SFinGe dataset(left) and FVC dataset (right).

5 CONCLUSION

In this study, we explored diverse self-supervised

learning techniques to pre-train a model for effective

ﬁngerprint representations in recognition and veriﬁ-

cation. A novel approach involved leveraging ﬁnger-

print enhancement as a self-supervised pre-training

method. Probing experiments assessed the effective-

ness of learned representations across various pre-

training strategies. Comparisons against SimCLR

v2, SimSiam, MoCo v2, and BYOL methods on

SFinGe and FVC datasets consistently showed our

method’s superior veriﬁcation performance. Notably,

our model excelled in similarity-based veriﬁcation,

underscoring its effectiveness in ﬁngerprint recogni-

tion tasks. However, models performed better on

the synthetic SFinGe dataset, hinting at potential bias

in the training set and real-world data complexities.

Future work will expand to diverse real-world ﬁn-

gerprint datasets, improving model generalizability.

We’ll also explore additional self-supervised methods

for enhanced adaptability to real-world complexities,

emphasizing the potential of self-supervised learning

in ﬁngerprint biometrics while pointing to areas for

exploration and reﬁnement.

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