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Comparative Analysis between Generalized Hough
Transform and Neural Networks in Fingerprint
Recognition

Ali Zerbo, Abdoulaye Sere

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To cite this version:

Ali Zerbo, Abdoulaye Sere. Comparative Analysis between Generalized Hough Transform and Neural Networks in Fingerprint Recognition. 2024. hal-04527740

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https://inria.hal.science/hal-04527740

Preprint submitted on 30 Mar 2024

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# Comparative Analysis between Generalized Hough Transform and Neural Networks in Fingerprint Recognition

Ali Zerbo, Abdoulaye Sere

► To cite this version:

Ali Zerbo, Abdoulaye Sere. Comparative Analysis between Generalized Hough Transform and Neural Networks in Fingerprint Recognition. 2025. hal-04527740v2

HAL Id: hal-04527740

https://inria.hal.science/hal-04527740v2

Preprint submitted on 18 Aug 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Comparative Analysis between Generalized Hough Transform and Neural
Networks in Fingerprint Recognition

Ali ZERBO¹ and Abdoulaye SERE²

¹PhD student, Burkina Faso

²Senior lecturer, Burkina Faso

*E-mail: alizerbo98@gmail.com

**Abstract**

This in-depth study aims to evaluate and compare the performance of two major methods in the field of fingerprint recognition: the generalized Hough transform and Siamese neural networks. To do this, we carried out an exhaustive comparison on a representative sample of 628 fingerprints for each of the models, using the CASIA database, recognised for its diversity and appropriate size for accurate evaluations.

The Siamese neural networks stood out by displaying remarkable accuracy, exceeding 99.2%. Their ability to process large datasets efficiently underlines their relevance in biometric scenarios requiring in-depth analysis. In contrast, the Generalized Hough Transform achieved a respectable accuracy rate of 97.7%, confirming its reliability even in more limited data contexts.

This analysis highlights the distinct advantages of each approach, with particular emphasis on the adaptability of Siamese neural networks to complex variations in fingerprints. Their ability to generalise models to large datasets demonstrates their robustness and potential for large-scale biometric applications.

To evaluate this performance, we used measures such as the confusion matrix, which provides a clear visualisation of the relationship between predicted and actual values, and the F1 score, which provides a balanced assessment of precision and recall.

The results discussed enrich our understanding of the issues involved in biometric security and open up promising prospects for future research in this constantly evolving field.

**Keywords**

Hough Transform; confusion matrix; F1-score

# I INTRODUCTION

Fingerprint recognition remains an essential pillar of biometric systems, playing a vital role in the identification and authentication of individuals, with diverse applications ranging from securing mobile devices to physical access control systems and forensics.

Faced with this growing importance, the scientific community is exploring various approaches aimed at improving the accuracy and efficiency of fingerprint recognition systems. These approaches include both traditional methods based on geometric features, such as the Hough
# Comparative Analysis between Generalized Hough Transform and Neural Networks in Fingerprint Recognition

Ali ZERBO¹ and Abdoulaye SERE²

¹PhD student, Burkina Faso

²Senior lecturer, Burkina Faso

*E-mail: alizerbo98@gmail.com

**Abstract**

This study evaluates and compares the performance of two prominent fingerprint recognition methods: the Generalized Hough Transform (GHT) and Siamese neural networks. Using the diverse and well suited CASIA database, we conducted an exhaustive comparison on a representative sample of 628 fingerprints per model. Siamese neural networks demonstrated exceptional accuracy, surpassing 99.2%. Their efficiency in processing large datasets highlights their suitability for biometric applications requiring detailed analysis. In contrast, the Generalized Hough Transform achieved a strong accuracy of 97.7%, proving its reliability even with more constrained data. Our analysis underscores the distinct strengths of each method, particularly the adaptability of Siamese networks to complex fingerprint variations. Their robust generalization capabilities make them ideal for large-scale biometric systems. Performance was assessed using key metrics, including the confusion matrix (visualizing predicted vs. actual matches) and the F1-score (balancing precision and recall). These findings advance our understanding of biometric security challenges and suggest promising directions for future research in this rapidly evolving field

**Keywords**

Hough Transform; confusion matrix; F1-score

## I INTRODUCTION

Fingerprint recognition remains a cornerstone of modern biometric systems, critically supporting identification and authentication across diverse applications ranging from mobile device security to forensic investigations. As reliance on these systems grows, the scientific community continues to pursue enhanced methods that improve both accuracy and operational efficiency. Current approaches span traditional geometric feature-based techniques, such as the Hough transform, to advanced deep learning architectures including convolutional neural networks (CNNs) [12][6].

Our study focuses on comparing two particularly significant yet fundamentally different approaches: the Generalized Hough Transform (GHT) and Siamese neural networks. This selection is motivated by their complementary advantages in addressing key fingerprint recognition challenges. While conventional CNNs excel at feature extraction, they typically require extensive labeled datasets and are less optimized for pairwise matching tasks. Siamese networks, by contrast, are specifically designed for similarity comparison, making them exceptionally suited
transform, and modern approaches exploiting advances in deep learning, such as convolutional neural networks (CNNs) [12][6].

In this context, our study aims to carry out a comprehensive performance evaluation of two distinct approaches to fingerprint recognition: the generalized Hough transform and the neural network method, more specifically Siamese neural networks. Siamese neural networks are a particular variant of neural networks used to compare pairs of inputs [7]. We have chosen the CASIA database, renowned for its diversity and size, as our testing ground.

Our aim is to compare the ability of these two methods to deal with complex variations in fingerprints, to provide accurate results and to adapt to realistic operational conditions. This evaluation goes beyond the simple measurement of accuracy and includes an in-depth analysis of the advantages and limitations of each method, providing crucial information for the design and deployment of reliable biometric systems.

To assess the performance of each method, we use key evaluation metrics, including the con- fusion matrix, which visualises the correspondence between the values predicted by the model and the actual values, and the F1 score, which provides a balanced measure of precision and recall by taking into account both false positives and false negatives.

This in-depth study, which combines the analysis of traditional and modern measures, aims to provide a holistic understanding of the comparative performance of the generalised Hough transform and the neural network method in the field of fingerprint recognition. The results ob- tained will be crucial in guiding the design and implementation of robust and reliable biometric systems.

## II METHOD DESCRIPTION

### 2.1 The database

The choice of database is of crucial importance in our study, as it is the essential testing ground for evaluating the performance of fingerprint recognition methods. We opted for the CASIA database, renowned for its reputation, substantial size and carefully organised diversity.

The CASIA database is large, comprising an extensive collection of fingerprints from a variety of sources. It is organised into 500 files numbered from "000" to "499", each representing a specific individual. Each subject is associated with two distinct sub-folders, 'L' (left) and 'R' (right), each containing 20 fingerprint images [14]. This is illustrated in the figure below.

This database contains a total of 20,000 fingerprint images, covering a wide variety of condi- tions, people and environments. The large size of the CASIA database plays a crucial role in obtaining robust and generalisable results, which are essential for evaluating the performance of fingerprint recognition methods in various scenarios. The diversity within the CASIA database is of paramount importance, as it accurately reflects the real conditions that fingerprint recog- nition systems face. Fingerprints come from a diverse range of individuals, including natural variations such as age, gender and ethnicity. In addition, the database captures different finger positions, varying levels of illumination and other environmental factors that contribute to its rich diversity [13].

In opting for the CASIA database, our aim is to ensure a comprehensive and representative evaluation of the performance of fingerprint recognition methods. This selection takes into
for fingerprint verification where assessing input pairs is essential [7]. The generalized Hough
transform offers distinct benefits of computational efficiency and noise robustness, maintaining
relevance for applications where interpretability and resource constraints are paramount. This
dual evaluation provides unique insights into how traditional and modern methods can address
different operational requirements.

Using the comprehensive CASIA database selected for its diversity and scalability, we conduct
a rigorous comparative analysis with three primary objectives: first, assessing each method’s
capability to handle complex fingerprint variations; second, evaluating their accuracy under
practical conditions; and third, providing a detailed examination of their respective strengths
and limitations.

Our evaluation employs robust metrics including confusion matrices for performance visualiza-
tion and F1 scores to balance precision and recall, offering a nuanced understanding of each
approach's effectiveness.

This systematic comparison not only advances our understanding of fingerprint recognition
methodologies but also provides practical guidance for implementing context-appropriate solu-
tions. By bridging traditional and cutting-edge approaches, our findings contribute valuable in-
sights for developing next-generation biometric systems that must balance accuracy, efficiency,
and scalability in real world applications.

## II METHOD DESCRIPTION

2.1 The database

The choice of database is crucial in our study, as it serves as the primary testbed for evaluat-
ing the performance of fingerprint recognition methods. We opted for the CASIA database,
renowned for its substantial size and carefully organized diversity.

The CASIA database is extensive, comprising a large collection of fingerprints from a variety
of sources. It is organized into 500 folders numbered from "000" to "499," each representing a
specific individual. Each subject has two subfolders, "L" (left) and "R" (right), each containing
20 fingerprint images [14]. This is illustrated in the figure below.

In total, the database comprises 20,000 fingerprint images, encompassing a wide range of con-
ditions, subjects, and environments. The scale of CASIA plays a crucial role in obtaining robust
and generalizable results, which are essential for evaluating performance across diverse scenar-
ios. Equally important is its diversity, as it reflects the real-world conditions faced by fingerprint
recognition systems. The fingerprints cover natural variations in age, gender, and ethnicity. In
addition, the database captures different finger positions, varying illumination levels, and other
environmental factors that contribute to its richness [13].

By opting for the CASIA database, we aim to ensure a comprehensive and representative eval-
uation of fingerprint recognition methods. This selection takes into account the complexity of
real-world conditions, providing a solid basis for relevant and applicable conclusions.
account the complexity of real-world conditions, thus providing a solid basis for relevant and
applicable conclusions.

<figure><img src="image_4.png" /><figcaption>Figure 1: Structure of the CASIA-FingerprintV5(BMP)_2 database.</figcaption></figure>

## 2.2 Hough Transform

The generalised Hough transform occupies a crucial position in the field of fingerprint recognition, playing the essential role of detecting and extracting specific features, such as minutiae, from an image. This method is based on the fundamental principle of the Hough transform, originally developed for detecting lines in images, and its generalised version extends this concept to detect more complex shapes, such as curves and circles, making it particularly suitable for fingerprint analysis [4].

In this section, we detail the implementation of the generalised Hough transform, highlighting the link between its generality and our study. We begin by describing the data pre-processing, during which fingerprint images are subjected to various techniques, such as intensity normalization, segmentation and binarisation, in order to remove noise. The Zhang-Suen algorithm is then used to reduce the thickness of the minutiae to the size of a pixel, making them easier to extract.

### a) Data preprocessing

The impression images were subjected to various pre-processing techniques [1][8], such as intensity normalization, segmentation, and binarization, to eliminate noise. Then, the Zhang-Suen algorithm [3] was applied to each block to reduce the thickness of the minutiae to the size of a pixel, thus facilitating their extraction.

Here is an overview of the various stages of fingerprint image preprocessing.
<figure><img src="image_3.png" /><figcaption>Figure 1: Structure of the CASIA-FingerprintV5(BMP)_2 database.</figcaption></figure>

## 2.2 Hough Transform

The Generalized Hough Transform (GHT) plays a crucial role in fingerprint recognition by detecting and extracting specific features such as minutiae from an image. It builds on the core principle of the original Hough transform, developed for line detection, and extends it to more complex shapes (e.g., curves and circles), making it particularly suitable for fingerprint analysis [4].

In this section, we detail the implementation of the Generalized Hough Transform and explain how its generality aligns with our study. We begin with data preprocessing, during which fingerprint images undergo intensity normalization, segmentation, and binarization to reduce noise. The Zhang-Suen thinning algorithm is then applied to reduce ridge thickness to a single-pixel width, thereby simplifying minutiae extraction.

### a) Data preprocessing

Fingerprint impressions were subjected to several preprocessing techniques [1][8] including intensity normalization, segmentation, and binarization to suppress noise. Subsequently, the Zhang-Suen thinning algorithm [3] was applied to each block to reduce ridge thickness to a single-pixel skeleton, thereby simplifying minutiae extraction.

An overview of the stages of fingerprint image preprocessing is provided below.
<figure><img src="image_5.png" /><figcaption>Figure 2: Stages of fingerprint image preprocessing.</figcaption></figure>

b) Minutiae extraction

The minutiae are unique characteristics of the fingerprint that allow for its identification and ver-
ification [2]. Each minutia $m_i$ is represented by a quadruplet of coordinates: $m_i = (x_i, y_i, \theta_i, t_i)$
[10]. The coordinates $x_i$ and $y_i$ correspond to the position of the minutia in the image, $\theta_i$
represents its direction, which is generally calculated from the local orientation of the ridge,
and $t_i$ indicates whether the minutia is a termination or a bifurcation type. Bifurcations and
terminations are key points for comparing fingerprints.

For each black pixel in each block of the fingerprint, we calculate the Crossing Number (CN) indicator [2], which is a measure of local topology. The formula for the CN indicator is as follows:

$$
CN = \frac{1}{2} \sum_{i=1}^{8} |p_i - p_{i+1}| \qquad (1)
$$

$$
\text{with } p_9 = p_1
$$

The neighboring pixels $p_1, \dots, p_9$ are arranged in a clockwise order. Using the properties of the
CN, as summarized in the table above, each ridge pixel can be classified as either a termination,
a bifurcation, or a point without a minutia. Specifically, an isolated pixel will have a CN of 0, a
termination will have a CN of 1, a continuous ridge will have a CN of 2, a bifurcation will have
a CN of 3, and a crossing will have a CN of 4 [2]. This information is essential for minutiae
extraction and fingerprint comparison.
<figure><img src="image_4.png" /><figcaption>Figure 2: Stages of fingerprint image preprocessing.</figcaption></figure>

## b) Minutiae extraction

The minutiae are unique characteristics of the fingerprint that allow for its identification and verification [2]. Each minutia $m_i$ is represented by a quadruplet of coordinates: $m_i = (x_i, y_i, \theta_i, t_i)$ [10]. The coordinates $x_i$ and $y_i$ correspond to the position of the minutia in the image, $\theta_i$ represents its direction, which is generally calculated from the local orientation of the ridge, and $t_i$ indicates whether the minutia is a termination or a bifurcation type. Bifurcations and terminations are key points for comparing fingerprints.

For each black pixel in each block of the fingerprint, we calculate the Crossing Number (CN) indicator [2], which is a measure of local topology. The formula for the CN indicator is as follows:

$$CN = \frac{1}{2} \sum_{i=1}^{8} |p_i - p_{i+1}| \quad \text{with } p_9 = p_1 \qquad (1)$$

The neighboring pixels $p_1, ..., p_9$ are arranged in a clockwise order. Using the properties of the CN, as summarized in the table above, each ridge pixel can be classified as either a termination, a bifurcation, or a point without a minutia. Specifically, an isolated pixel will have a CN of 0, a termination will have a CN of 1, a continuous ridge will have a CN of 2, a bifurcation will have a CN of 3, and a crossing will have a CN of 4 [2]. This information is essential for minutiae extraction and fingerprint comparison.

## c) Minutia orientation estimation

To estimate the orientation of a minutia $m$, we seek to find the line that passes through $m$ and best fits the surrounding connected points. We use a simple linear regression method [13]
### c) Calculation of minutiae orientation

To determine the orientation of a minutia *m*, we seek to find the line that passes through *m* and that best fits the surrounding connected points. We use a simple linear regression method [13] for this purpose. By then applying the atan function to the slope of the line, we obtain the orientation of the minutia.

### d) Comparison of minutiae

After the pre-processing phase of the fingerprint image, we use an adapted Hough transform algorithm, followed by a matching algorithm to detect and connect the corresponding feature points. The adapted Hough transform algorithm uses mathematical equations in a parameter space to determine the spatial relationships between the feature points. These parameters describe the characteristics of the spatial relationships, such as the distance and angle between the minutiae.

The algorithms for the adapted Hough transform and the matching are illustrated below, respectively:

<caption>**Algorithm II.1 : Generalized Hough Transform Algorithm**</caption>

1. Input: Two sets of minutiae
2. Output: The parameter transformation
3. minutia as M in algorithm
4. Start by initializing an accumulation matrix.
5. For each M in the first set:
6. For each M in the second set:
7. Calculate the angle difference between the two minutiae:
8. angle_diff = min(||angle_M_1 - angle_M_2||, 360 - ||angle_M_1 - angle_M_2||)
9. angle_diff = 360_degree - angle_diff
10. position_diff_x = x_M_1 - (x_M_2 * cos(angle_diff) - y_M_2 * sin(angle_diff))
11. position_diff_y = y_M_1 - (x_M_2 * sin(angle_diff) + y_M_2 * cos(angle_diff))
12. accumulation_matrix[ position_diff_x ][ position_diff_y ] += 1
13. return the peak position in the accumulation matrix.

<caption>**Algorithm II.2 : Matching of two sets of minutiae**</caption>

1. Input: Two sets of minutiae
2. Output: List of paired minutiae
3. Minutia as M in algorithm threshold as th
4. Initialization of the list and the counter count
5. For each M in the first set:
6. For each M in the second set:
7. If position_diff_x < th1 and position_diff_y < th1 and angle_diff < th2
8. count = count + 1
9. list [count] = (M_first_set, M_second_set)
10. return list of minutiae paired

where th1 and th2 represent the distortion tolerance thresholds.
for this purpose. By then applying the atan function to the slope of the line, we obtain the orientation of the minutia.

### d) Comparison of minutiae

After the preprocessing phase of the fingerprint image, we use an adapted Generalized Hough Transform (GHT) algorithm, followed by a matching algorithm to detect and connect the corresponding feature points. The adapted Generalized Hough Transform (GHT) algorithm uses mathematical equations in a parameter space to determine the spatial relationships between the feature points. These parameters describe the characteristics of the spatial relationships, such as the distance and angle between the minutiae.

The algorithms for the adapted Generalized Hough Transform (GHT) and the matching are illustrated below, respectively:

**Algorithm 1 Generalized Hough Transform Algorithm**

**Require:** Two sets of minutiae $S_1$ and $S_2$

**Ensure:** Parameter transformation (peak position)

1: Initialize accumulation matrix $A[x][y] \leftarrow 0$

2: **for** each minutia $M_1 \in S_1$ **do**

3: **for** each minutia $M_2 \in S_2$ **do**

4: $\delta_{angle} \leftarrow \min(|\theta_{M_1} - \theta_{M_2}|, 360 - |\theta_{M_1} - \theta_{M_2}|)$

5: $angle_{diff} \leftarrow 360 - \delta_{angle}$

6:

7: $\Delta x \leftarrow x_{M_1} - (x_{M_2} \cdot \cos(\theta_{diff}) - y_{M_2} \cdot \sin(\theta_{diff}))$

8: $\Delta y \leftarrow y_{M_1} - (x_{M_2} \cdot \sin(\theta_{diff}) + y_{M_2} \cdot \cos(\theta_{diff}))$

9:

10: $A[\Delta x][\Delta y] \leftarrow A[\Delta x][\Delta y] + 1$

11: **end for**

12: **end for**

13: **return** $\arg\max_{x,y} A[x][y]$

**Algorithm 2 Minutiae matching**

**Require:** Two minutiae sets $E_1$ and $E_2$, thresholds $\tau_1$ and $\tau_2$

**Ensure:** List of minutiae pairs $\mathcal{P}$

1: Initialize $\mathcal{P} \leftarrow \emptyset, count \leftarrow 0$

2: **for** each $M_1 \in E_1$ **do**

3: **for** each $M_2 \in E_2$ **do**

4: **if** $|\Delta x| < \tau_1$ **and** $|\Delta y| < \tau_1$ **and** $\theta_{diff} < \tau_2$ **then**

5: $count \leftarrow count + 1$

6: $\mathcal{P}[count] \leftarrow (M_1, M_2)$

7: **end if**

8: **end for**

9: **end for**

10: **return** $\mathcal{P}$
III NEURAL NETWORK MODEL FOR IMAGE COMPARISON

Remarkable progress in the field of artificial intelligence has spawned a plethora of neural net-
work architectures specialised for specific tasks. For fingerprint verification and image compari-
son, the adoption of Siamese models has emerged as a particularly effective approach [4,6]. The
model we describe below is in this vein, adopting a Siamese architecture for image comparison,
thus exploiting the advantages inherent in this convolutional structure.

Our methodology is based on the use of a neural network specifically designed for fingerprint
recognition. The choice of this particular model, the details of which are set out below, stems
from a series of considerations aimed at optimising recognition performance in a variety of
contexts. Before exploring the characteristics of the network in depth, it is essential to empha-
sise the crucial role of methodology in evaluating the performance of fingerprint recognition
methods.

To make our methodological approach more accessible, we will include a detailed schematic
diagram here

<figure><img src="image_6.png" /><figcaption>Figure 3: Diagram.</figcaption></figure>

This diagram will provide readers with a clear view of the different stages in the process, from
data preparation to the crucial minutiae comparison phase. The inclusion of this diagram is
intended to provide a global perspective, making it easier to understand the interaction between
the different components of our methodology.

**3.1 Model architecture**

The architecture of the fingerprint matching model is based on a Siamese approach. This
method exploits two input branches sharing the same weights to extract distinct features from
two fingerprint images, as illustrated in Figure 4. The main objective is to measure the simi-
larity between these fingerprints, focusing on subtle differences [7]. The main objective is to
measure the similarity between these fingerprints, focusing on subtle differences.
III NEURAL NETWORK MODEL FOR IMAGE COMPARISON

Remarkable advances in artificial intelligence have produced a wide range of neural network architectures specialized for specific tasks. For fingerprint verification and image comparison, Siamese models have emerged as a particularly effective approach [4,6]. The model described below follows this paradigm, adopting a Siamese architecture for image comparison and thereby leveraging convolutional feature learning.

Our methodology employs a neural network specifically designed for fingerprint recognition. The choice of this model detailed below stems from considerations aimed at optimizing recognition performance across diverse conditions. Before delving into the network's characteristics, we emphasize the central role of methodology in evaluating fingerprint recognition systems.

To make the methodological approach more accessible, a detailed schematic diagram is provided below.

<figure><img src="image_5.png" /><figcaption>Figure 3: Diagram.</figcaption></figure>

This diagram gives readers a clear view of the stages of the process from data preparation through the crucial minutiae matching stage. Including the diagram offers a high-level overview that clarifies the interactions among the components of our methodology.

**3.1 Model architecture**

The fingerprint-matching model adopts a Siamese architecture with two weight-sharing branches that process a pair of fingerprint images (Figure 4). Each branch encodes its input into a feature embedding, and a similarity measure computed from the two embeddings determines whether the prints match, with sensitivity to subtle differences [7].
<figure><img src="image_7.png" /><figcaption>Figure 4: Model architecture.</figcaption></figure>

## 3.2 Feature extraction

The first stage of the model involves feature extraction from fingerprint images. This phase uses convolution and max-pooling operations to detect significant patterns while reducing the dimensionality of the data. The special feature lies in the sharing of weights between the two input branches, ensuring consistent feature extraction.

## 3.3 Subtracting Features

Once the features have been extracted, a subtraction operation is performed between the two input branches. The aim of this step is to highlight the pixel-by-pixel differences between the fingerprints, highlighting areas of divergence.

## 3.4 Classification

The resulting features are then passed through classification layers, including additional convolution operations. These decision layers determine the probability of similarity between the fingerprints. The sigmoid activation function at the output layer produces a value between 0 and 1, indicating the degree of similarity.
<figure><img src="image_6.png" /><figcaption>Figure 4: Model architecture.</figcaption></figure>

## 3.2 Feature extraction

The first stage of the model involves feature extraction from fingerprint images. This phase uses convolution and max-pooling operations to detect significant patterns while reducing the dimensionality of the data. The special feature lies in the sharing of weights between the two input branches, ensuring consistent feature extraction.
## 3.5 Data Augmentation Training

To improve the robustness of the model, we integrated a data generator during the learning phase. This generator applies different augmentation techniques, such as Gaussian blur and geometric transformations, to diversify the dataset. In our case, we increased the number of images by five for each footprint. This additional increase is intended to further enrich the dataset, giving the model a greater variety of cases to deal with. This diversification is essential to enable the model to generalise better in the presence of variations in fingerprints, thus helping to improve its robustness and its ability to recognise fingerprints effectively in real-life situations. An example illustrating this increase in data is shown in Figure 5.

<figure><img src="image_8.png" /><figcaption>Figure 5: Data Augmentation.</figcaption></figure>

With regard to the model parameters, the activation function used is ReLU (Rectified Linear Unit) for the convolutional layers, except for the output layer, which uses the sigmoid activation function. The optimizer used is Adam, an algorithm commonly used in deep learning, with the default learning rate. For the loss function, we opted for the binary cross-entropy loss, adapted to binary classification tasks. Finally, the batch size used for training is 563, determining the number of samples processed simultaneously before the model weights are updated. We also trained the model over 6 epochs, a number chosen to ensure adequate convergence while avoiding overlearning. A summary of the model is presented below:

Model: "functional_47"

<table><thead><tr><th>Layer (type)</th><th>Output Shape</th><th>Param #</th><th>Connected to</th></tr></thead><tbody><tr><td>inputLayer_33<br>(InputLayer)</td><td>(None, 90, 90, 1)</td><td>0</td><td>-</td></tr><tr><td>inputLayer_34<br>(InputLayer)</td><td>(None, 90, 90, 1)</td><td>0</td><td>-</td></tr><tr><td>functional_45<br>(Functional)</td><td>(None, 22, 22, 32)</td><td>9,568</td><td>inputLayer_33[0]...<br>inputLayer_34[0]...</td></tr><tr><td>subtract_11<br>(Subtract)</td><td>(None, 22, 22, 32)</td><td>0</td><td>functional_45[0]...<br>functional_45[1]...</td></tr><tr><td>conv2d_35 (Conv2D)</td><td>(None, 22, 22, 64)</td><td>18,496</td><td>subtract_11[0][0]</td></tr><tr><td>max_pooling2d_35<br>(MaxPooling2D)</td><td>(None, 11, 11, 64)</td><td>0</td><td>conv2d_35[0][0]</td></tr><tr><td>flatten_11<br>(Flatten)</td><td>(None, 7744)</td><td>0</td><td>max_pooling2d_35...</td></tr><tr><td>dense_22 (Dense)</td><td>(None, 64)</td><td>495,680</td><td>flatten_11[0][0]</td></tr><tr><td>dense_23 (Dense)</td><td>(None, 1)</td><td>65</td><td>dense_22[0][0]</td></tr></tbody></table>

Total params: 523,809 (2.00 MB)

Trainable params: 523,809 (2.00 MB)

Non-trainable params: 0 (0.00 B)

<caption>Figure 6: Model summary.</caption>
## 3.3 Feature Subtraction

Once features have been extracted, an element-wise subtraction is applied between the two branches. This step reveals pixelwise differences between the fingerprints, thereby emphasizing regions of divergence.

## 3.4 Classification

The resulting features are then passed through classification layers, including additional convolutional operations. These decision layers estimate the probability of similarity between the fingerprints. The sigmoid activation in the output layer produces a value in [0, 1] that represents the similarity score.

## 3.5 Data Augmentation Training

To improve robustness, we integrated a data generator during training. This generator applies various augmentation techniques such as gaussian blur and geometric transformations to diversify the dataset. In our setup, we increased the number of images fivefold per fingerprint. This enlargement further enriches the dataset, giving the model greater variety and helping it generalize in the presence of fingerprint variations, which in turn improves robustness and recognition performance in real world conditions. An illustration of the augmentation pipeline is shown in Figure 5.

<figure><img src="image_7.png" /><figcaption>Figure 5: Data Augmentation.</figcaption></figure>

With regard to the model parameters, the activation function used is ReLU (Rectified Linear Unit) for the convolutional layers, except for the output layer, which uses the sigmoid activation function. The optimizer used is Adam, an algorithm commonly used in deep learning, with the default learning rate. For the loss function, we opted for the binary cross-entropy loss, adapted to binary classification tasks. Finally, the batch size used for training is 563, determining the number of samples processed simultaneously before the model weights are updated. We also trained the model over 6 epochs, a number chosen to ensure adequate convergence while avoiding overfitting. Summaries of the model architecture and hyperparameters are provided below (Tables 1–3).
IV RESULTS

In our study, we used two distinct methods to analyse an image dataset: the Hough transform method and the neural network method.

For the Hough transform method, we randomly selected 628 images from our main dataset. This selection was used to create two subsets of the data, Data1 and Data2, containing 500 and 128 images respectively. Note that 64 images are common to these two subsets. We then compared the fingerprints in Data2 with those in Data1.

For the neural network approach, we divided our dataset (20,000 fingerprint images) by allocating 90% of the images to model training and the remaining 10% to validation. After training, a random sample of 128 images was selected for further testing. These tests were used to compare the results obtained with those of the Hough transformation.

These evaluations were carried out on a Lenovo Ideapad Gaming 3 laptop, equipped with 16 GB of RAM and a 10th-generation Intel Core i5 processor running at 4.5 GHz. Python (version 3.11.7) was used as the main development environment for implementing the machine learning models. TensorFlow (version 2.15.0) was used to create, train and evaluate neural networks, in particular for the Siamese neural network method. Anaconda was used as the development environment, with Jupyter Notebook for executing and presenting the Python code. All these operations were carried out on the same machine to ensure consistency of experimental conditions.

Thanks to these rigorous methodological approaches, we have obtained precise and reliable results, highlighting the distinct performance of each analysis method. The results are presented in the table 1.

These results are also illustrated using a confusion matrix and a diagram, providing a clear visualisation of the performance of each method.

<figure><img src="image_9.png" /><figcaption>Figure 7: Confusion matrix.</figcaption></figure>
<table><caption>Table 1: Architecture of the shared feature extraction block</caption><thead><tr><th>Layer</th><th>Type</th><th>Size</th><th>Output</th><th>Params</th></tr></thead><tbody><tr><td>Input</td><td>InputLayer</td><td>-</td><td>90×90×1</td><td>0</td></tr><tr><td>Conv2D_1</td><td>Conv2D</td><td>3×3</td><td>90×90×32</td><td>320</td></tr><tr><td>MaxPool_1</td><td>MaxPool2D</td><td>2×2</td><td>45×45×32</td><td>0</td></tr><tr><td>Conv2D_2</td><td>Conv2D</td><td>3×3</td><td>45×45×32</td><td>9,248</td></tr><tr><td>MaxPool_2</td><td>MaxPool2D</td><td>2×2</td><td>22×22×32</td><td>0</td></tr><tr><td colspan="4">Total:</td><td>9,568</td></tr></tbody></table>

<table><caption>Table 2: Complete Siamese neural network architecture</caption><thead><tr><th>Stage</th><th>Description</th><th>Output Shape</th><th>Parameters</th></tr></thead><tbody><tr><td>Inputs</td><td>Two 90×90 grayscale images</td><td>(None, 90, 90, 1) × 2</td><td>0</td></tr><tr><td>Feature Extraction</td><td>Shared feature_model application</td><td>(None, 22, 22, 32) × 2</td><td>9,568</td></tr><tr><td>Subtraction</td><td>Element-wise difference</td><td>(None, 22, 22, 32)</td><td>0</td></tr><tr><td>Conv2D</td><td>Convolution on difference</td><td>(None, 22, 22, 64)</td><td>18,496</td></tr><tr><td>MaxPooling</td><td>Spatial reduction</td><td>(None, 11, 11, 64)</td><td>0</td></tr><tr><td>Flatten</td><td>Vectorization</td><td>(None, 7,744)</td><td>0</td></tr><tr><td>Dense_1</td><td>Intermediate dense layer</td><td>(None, 64)</td><td>495,680</td></tr><tr><td>Dense_2</td><td>Binary output</td><td>(None, 1)</td><td>65</td></tr><tr><td colspan="3">Total parameters:</td><td>523,809</td></tr></tbody></table>

<table><caption>Table 3: Siamese network hyperparameters</caption><thead><tr><th colspan="2">Category</th><th>Value</th></tr></thead><tbody><tr><td rowspan="5">Architecture</td><td>Input size</td><td>90×90×1</td></tr><tr><td>Convolution kernels</td><td>3×3</td></tr><tr><td>Pooling</td><td>2×2 MaxPooling</td></tr><tr><td>Filters</td><td>32 → 32 → 64</td></tr><tr><td>Padding</td><td>'same'</td></tr><tr><td rowspan="3">Training</td><td>Loss function</td><td>binary crossentropy</td></tr><tr><td>Optimizer</td><td>adam</td></tr><tr><td>Metric</td><td>accuracy</td></tr></tbody></table>

## IV RESULTS

In our study, we used two distinct methods to analyse an image dataset: Generalized Hough Transform (GHT) and the neural network.

For Generalized Hough Transform, we randomly selected 628 images from our main dataset. This selection was used to create two subsets of the data, Data1 and Data2, containing 500 and 128 images respectively. Note that 64 images are common to these two subsets. We then compared the fingerprints in Data2 with those in Data1.

For the neural network approach, we divided our dataset (20,000 fingerprint images) by allocating 90% of the images to model training and the remaining 10% to validation. After training, a random sample of 128 images was selected for further testing. These tests were used to compare the results obtained with those of the Hough transformation.

These evaluations were carried out on a Lenovo Ideapad Gaming 3 laptop, equipped with 16 GB of RAM and a 10th-generation Intel Core i5 processor running at 4.5 GHz. Python (version 3.11.7) was used as the main development environment for implementing the machine learning
<figure><img src="image_10.png" /><figcaption>Figure 8: grouped bar graph of analysis method performance.</figcaption></figure>

<table><caption>Table 1: Performance comparison of analyzed methods.</caption><thead><tr><th></th><th>Accuracy</th><th>Sensitivity</th><th>Specificity</th><th>F1-score</th></tr></thead><tbody><tr><td>Generalized Hough Transform</td><td>0.977</td><td>0.984</td><td>0.969</td><td>0.976</td></tr><tr><td>Siamese Neural Networks</td><td>0.992</td><td>1.0</td><td>0.984</td><td>0.992</td></tr></tbody></table>

# V DISCUSSION

## 5.1 Performance comparison

In this section, we compare the performance of the generalized Hough transform and neural networks, based on our results.

Our observations show that the neural network method slightly outperforms the generalized Hough transform. Indeed, the accuracy of neural networks is slightly higher (0.992) than that of the Hough transform (0.977). In addition, the perfect sensitivity of 1.0 of the neural networks demonstrates a remarkable ability to correctly identify positive fingerprints, while the sensitivity of the Hough transform is slightly lower (0.984). In terms of specificity, the two methods performed comparably well, with values of 0.984 for neural networks and 0.969 for the Hough transform. Finally, the F1 score, which assesses the balance between precision and sensitivity, is also slightly higher for neural networks (0.992) than for the Hough transform (0.976).

These results suggest that Siamese neural networks perform better for the fingerprint identification task than the generalized Hough transform method, particularly in terms of sensitivity and F1 score. However, it is important to point out that the Hough transform also performs well and can be a viable option in certain situations, particularly when computing resources are limited or when the available data is structured in such a way as to facilitate its use.
models. TensorFlow (version 2.15.0) was used to create, train and evaluate neural networks, in particular for the Siamese neural network method. Anaconda was used as the development environment, with Jupyter Notebook for executing and presenting the Python code. All these operations were carried out on the same machine to ensure consistency of experimental conditions.

Thanks to these rigorous methodological approaches, we have obtained precise and reliable results, highlighting the distinct performance of each analysis method. The results are presented in the table 1.

These results are also illustrated using a confusion matrix and a diagram, providing a clear visualisation of the performance of each method.

<figure><img src="image_8.png" /><figcaption>Figure 6: Confusion matrix.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 7: grouped bar graph of analysis method performance.</figcaption></figure>
## 5.2 Limitations of the study

For an accurate interpretation of the findings of our study, it is essential to take into account its limitations. First, although the CASIA-FingerprintV5(BMP)2 database includes 20,000 fingerprint images, it represents only a fraction of the diversity of human fingerprints. Therefore, the models trained and evaluated on this database may not be fully generalizable to other datasets with a wider variety of fingerprints.

Secondly, our study focuses on the evaluation of two specific techniques: the generalised Hough transform and Siamese neural networks. Although these techniques were chosen for their relevance to fingerprint identification, other methods may offer different or complementary perspectives. Thus, our findings may not be generalizable to other fingerprint identification techniques.

Thirdly, the performance of the models we have developed may be sensitive to the parameters chosen during training, such as model hyperparameters and optimisation methods. Variations in these parameters could lead to different performances, requiring further exploration for a full understanding of the models' capabilities.

Finally, different experimental conditions, such as the hardware used, software libraries and versions of machine learning frameworks, may also influence the results. It is therefore crucial to take these factors into account when interpreting the performance of the models and their applicability in real-life contexts.

## VI CONCLUSION

In conclusion, this comparative study examined the performance of the generalised Hough transform and Siamese neural networks in the field of fingerprint recognition, revealing significant results. Siamese neural networks, with an accuracy of 0.992 and an F1 score of 0.992, demonstrated a slight superiority over the Hough transform, which obtained an accuracy of 0.977 and an F1 score of 0.976, underlining the effectiveness of this approach in this specific context.

This comparison provides valuable information for researchers and practitioners in the field of fingerprint recognition, highlighting the strengths and limitations of each method. These findings are crucial in guiding the development and adoption of more accurate and robust biometric recognition technologies, helping to enhance security and privacy in a variety of applications. However, despite these promising results, it is important to note that challenges remain, such as improving resistance to variations in fingerprint quality and managing large volumes of data. Future research is therefore needed to further explore these issues and develop even more effective solutions in the field of fingerprint recognition.

In terms of future prospects, an interesting approach could be to explore the possibility of merging the two methods studied. By combining the robustness and speed of calculation of the generalised Hough transform with the precision and generalisation of Siamese neural networks, it would be possible to create a more powerful and adaptable hybrid approach. This fusion of methods could open up new avenues for further improving fingerprint recognition and responding to current challenges in the field.
<table><caption>Table 4: Performance comparison of analyzed methods.</caption><thead><tr><th></th><th>Accuracy</th><th>Sensitivity</th><th>Specificity</th><th>F1-score</th></tr></thead><tbody><tr><td>Generalized Hough Transform</td><td>0.977</td><td>0.984</td><td>0.969</td><td>0.976</td></tr><tr><td>Siamese Neural Networks</td><td>0.992</td><td>1.0</td><td>0.984</td><td>0.992</td></tr></tbody></table>

# V DISCUSSION

## 5.1 Performance comparison

In this section, we compare the performance of the Generalized Hough Transform and the Siamese network based on the results obtained.

Our findings indicate that the Siamese network slightly outperforms the Generalized Hough Transform. It achieves higher accuracy (0.992 vs. 0.977) and perfect sensitivity (1.000 vs. 0.984), reflecting an excellent ability to correctly identify positive matches. Specificity remains comparable (0.984 for the Siamese model vs. 0.969 for Hough). Finally, the F1 score which balances precision and recall is also higher for the Siamese network (0.992 vs. 0.976).

Although the Siamese network is generally superior, the optimal choice depends on constraints related to data, computational resources, and interpretability requirements. Generalized Hough Transform (GHT) does not require labeled data, has a very small memory footprint, runs efficiently on CPU only hardware, and offers strong interpretability (votes/peaks in parameter space) all crucial for auditability and deterministic behavior. Conversely, a Siamese network requires labeled pairs and a machine-learning runtime, but typically provides greater robustness to noise, occlusions, and pose variations especially when trained with data augmentation. It also scales better to large databases by comparing compact vector representations (embeddings) via approximate nearest-neighbor search. In practice, prefer GHT under stringent resource or transparency constraints, and favor the Siamese approach when accuracy under diverse real-world conditions and scalability are paramount. A pragmatic compromise is a cascaded design: use fast geometric pre-filtering (GHT) to reject obvious non-matches or estimate coarse alignment, then apply the Siamese verifier to the remaining candidates to optimize the compute accuracy trade-off.

Under real-time constraints (decisions within a few tens of milliseconds) and limited resources (CPU-only, battery constrained), GHT remains highly competitive: predictable compute cost, minimal memory, no ML runtime, and deterministic behavior. In practice, one bounds the parameter space (angles, distances/radii), restricts processing to a region of interest (ROI), and applies early reject heuristics to meet strict service level objectives (SLOs). A Siamese network can also satisfy real-time requirements provided it is optimized: quantization (e.g., INT8), reduced input resolution, lightweight architecture (depthwise-separable convolutions), and early exit thresholds. With an embedded GPU/NPU, inference becomes very fast; on CPU, latency remains acceptable if the model is compact.

In structured environments (standardized acquisition: slap/roll scanners, glass platen, stable illumination, controlled pose and resolution), ridge geometry is predictable and intrinsic image quality is high. GHT then benefits fully from tightened parameter ranges, which reduces the accumulator size, accelerates decision-making, and strengthens interpretability (sharp peaks in the voting space), with low maintenance (no training or recalibration). This setting suits access kiosks, administrative counters, or industrial devices. Siamese networks often retain a
# REFERENCES

## Publications

[1] K. J. Anil, P. Salil, and P. Sharath. "Twin Test: On Discriminability of Fingerprints". In: *International Conference on Audio- and Video-Based Biometric Person Authentication* 2091.1629 (2001).

[2] W. Łukasz. "A minutae based matching algorithms in fingerprint recognition". In: *In medical informatics and technologies* 13.6 (2009).

[3] R. W. Anastasia. "Comparing Hilditch, Rosenfeld, Zhang-Suen, and Nagendraprasad Wang Gupta Thinning". In: *World Academy of Science, Engineering and Technology* 78.6 (2011).

[4] P. Mukhopadhyay and B. B. Chaudhuri. "A survey of Hough Transform". In: *Pattern Recognition* 48.1-3 (2015).

[5] V. C. Jane, P. Jianmin, N. R. Shesh, and G. Susan. "ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves". In: *Surgery* 159.6 (2016).

[6] A. Mouad, H. M. Vivek, P. Yannawar, and A. T. Gaikwad. "Overview of Fingerprint Recognition System". In: *International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)* (2016).

[7] D. Chicco. "Siamese Neural Networks: An Overview". In: *Artificial Neural Networks (Methods in Molecular Biology)* 2190 (2020).

[8] G. M. Rashmi, K. Deepti, and G. Rubén. "Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction". In: *Information Sciences* 530 (2020).

[9] J. W. Wong and S. Lai. "Multi-task CNN for restoring corrupted fingerprint images". In: *Pattern Recognition* 101.2 (2020).

[10] K. Abdullah, L. Asif, and A. Shafique. "Machine Learning in Computer Vision: A Review". In: *EAI Endorsed Transactions on Scalable Information Systems* 8.32 (2021).

[11] G. Jochen, H. Fred, M. Dominik, W. Kanit, R. Donghao, N. Rahul, K. Marc, and P. Kayur. "Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels". In: *CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems* 408 (2022).

[12] C. Mahmud and I. Masudul. "Contactless Fingerprint Recognition Using Deep Learning—A Systematic Review". In: *Journal of Cybersecurity and Privacy* 2.1-3 (2022), pages 714-730.

[13] S. Sujana and K. S. v. Reddy. "Multi-modal Biometric System for Face and Fingerprint using Convolutional Neural Network". In: *International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC)* (2022).

[14] Database. http://www.idealtest.org/. Accédé: 2024-01-29.

[15] docplay. https://docplayer.fr/222013326-Cs-107-mini-projet-1-reconnaissance-d-empreintesdigitales. Accédé: 202-03-30.
slight accuracy edge, but the gap may become marginal in highly controlled conditions; that gain should be weighed against deployment complexity (ML runtime, model updates). Thus, in highly structured environments with strong auditability requirements, GHT is preferable; where residual variability is present (partial/wet fingers, micro shifts) or when maximal accuracy is required, the Siamese network is the better choice.

## 5.2 Limitations of the study

To interpret our results appropriately, it is important to consider the study's limitations. First, although the CASIA-FingerprintV5 (BMP) dataset contains 20,000 fingerprint images, it captures only a fraction of the diversity found in human fingerprints. Models trained here may therefore exhibit biases and may not fully generalize to more heterogeneous datasets. For example, certain demographic groups or acquisition conditions may be underrepresented. To mitigate this, one could employ data augmentation (e.g., rotation, elastic distortion, contrast variation), incorporate more diverse datasets, and perform cross-dataset validation.

Second, we evaluated only two techniques (GHT and Siamese networks). Other approaches such as triplet-based networks, graph based methods, or Transformer-based architectures may offer different or complementary perspectives. Our conclusions should therefore not be assumed to generalize to all fingerprint identification or verification methods.

Third, the observed performance can be sensitive to training choices: hyperparameters, loss functions, optimization methods, the strategy for generating pairs (positive/negative), and the selection of a decision threshold. A systematic exploration (sensitivity analysis, hyperparameter search) would be necessary to more fully characterize model robustness.

Finally, experimental conditions hardware, software libraries, framework versions, and random seeds can also affect results. These factors should be accounted for when interpreting performance and assessing real world applicability. Using reproducible environments (e.g., containers), standardized benchmarks, fixing random seeds, and reporting confidence intervals can further strengthen the reliability of the conclusions.

# VI CONCLUSION

In conclusion, this comparative study examined the performance of the Generalized Hough Transform (GHT) and Siamese neural networks for fingerprint recognition and highlighted significant results. The Siamese model achieved higher accuracy (0.992 vs. 0.977) and a higher F1 score (0.992 vs. 0.976) than GHT, underscoring its effectiveness in this setting.

This comparison offers practical guidance by clarifying the strengths and limitations of each approach, which can inform the design and adoption of more accurate and robust biometric systems that enhance security and privacy across applications. Nonetheless, challenges remain improving robustness to variable fingerprint quality and scaling to large data volumes among them so further work is needed to address these issues and deliver more effective solutions.

To move beyond an either or choice, we propose a cascaded hybrid. GHT first provides a fast, interpretable pre selection of candidates (bounded parameter space, geometric score) and, if needed, a light geometric realignment; the shortlisted pairs are then refined by the Siamese network, which outputs a match probability. Final decisions can use dual thresholds (reject/accept/gray zone) and late score fusion (e.g., weighted or logistic calibration of the GHT score
with the network output). This strategy preserves GHT's frugality and transparency while leveraging the learned model's robustness. At very large scales, the order can be inverted: retrieve top-K candidates in the Siamese embedding space and validate them geometrically with GHT. Future evaluations should quantify the overall benefit (F1, ROC-AUC/EER) alongside latency and resource constraints.

## VII GLOSSARY OF TECHNICAL TERMS

<table><thead><tr><td>Term</td><td>Description</td></tr></thead><tbody><tr><td>Minutiae</td><td>Local ridge characteristics (e.g., terminations, bifurcations) used to describe and compare fingerprints.</td></tr><tr><td>Termination / Bifurcation</td><td>Types of minutiae: a termination (end of ridge) or a bifurcation (division into two branches).</td></tr><tr><td>Ridge orientation (theta)</td><td>Local direction of ridge flow around a point/minutia; used to guide matching and normalization.</td></tr><tr><td>Crossing Number (CN)</td><td>Local topological measure on a 3 × 3 neighborhood to classify pixels (non-minutia, termination, bifurcation, etc.). Formula: $CN \approx \frac{1}{2} \sum_{i=1}^{8} |p_i - p_{i+1}|$ with $p_9 = p_1$.</td></tr><tr><td>Generalized Hough Transform (GHT)</td><td>Voting-based method accumulating geometric evidence in a parameter space (Hough) to detect shapes or correspondences.</td></tr><tr><td>Accumulator (Hough space)</td><td>Discrete parameter grid where votes are tallied; peaks indicate the most probable geometric alignments.</td></tr><tr><td>Region of Interest (ROI)</td><td>Sub-part of the image containing the relevant fingerprint area, used to focus computations.</td></tr><tr><td>Siamese neural network</td><td>Two-branch network sharing the same weights, producing comparable embeddings to estimate similarity.</td></tr><tr><td>Embedding</td><td>Compact feature vector produced by a network to represent an image (for comparison or indexing).</td></tr><tr><td>Absolute difference / Subtraction</td><td>Elementary operations between two feature maps: $|A - B|$ (order-invariant) or $A - B$ (order-sensitive).</td></tr><tr><td>Precision</td><td><u>TP</u> / <u>TP+FP</u>: proportion of correctly predicted matches.</td></tr><tr><td>Recall (Sensitivity)</td><td><u>TP</u> / <u>TP+FN</u>: proportion of true matches correctly detected.</td></tr></tbody></table>
<table><caption>Table 5: Glossary of technical terms in fingerprint recognition and machine learning.</caption><tr><td>Specificity</td><td><sup>TN</sup>&frasl;<sub>TN+FP</sub>: proportion of non-matches correctly rejected.</td></tr><tr><td>F1-score</td><td>Harmonic mean of precision and recall:<br>F1 = 2 &times; <sup>Precision &times; Recall</sup>&frasl;<sub>Precision + Recall</sub>.</td></tr><tr><td>ROC-AUC / EER</td><td><strong>ROC-AUC</strong>: area under the ROC curve (threshold-independent performance).<br><strong>EER</strong>: equal error rate (false acceptances = false rejections).</td></tr><tr><td>Approximate Nearest Neighbor (ANN) search</td><td>Fast search for the K nearest neighbors in large databases (sub-linear time).</td></tr><tr><td>Quantization (INT8)</td><td>Representation of weights/activations on 8 bits to reduce memory and latency (minor precision loss).</td></tr><tr><td>NPU</td><td>Neural Processing Unit; hardware accelerator optimized for neural network inference on embedded devices.</td></tr><tr><td>Data augmentation</td><td>Transformations applied to training images (blur, rotation, contrast, etc.) to improve generalization.</td></tr><tr><td>Batch size / Epoch</td><td><strong>Batch size</strong>: number of samples processed before an update. <strong>Epoch</strong>: one complete pass through the training set.</td></tr><tr><td>ReLU / Sigmoid</td><td>Activation functions: ReLU for hidden layers, Sigmoid for binary outputs (match probability).</td></tr><tr><td>Binary cross-entropy</td><td>Cost function for binary classification, comparing predicted probabilities to actual labels.</td></tr><tr><td>Adam optimizer</td><td>Adaptive stochastic gradient method commonly used for neural network training.</td></tr></table>

## REFERENCES

### Publications

[1] K. J. Anil, P. Salil, and P. Sharath. "Twin Test: On Discriminability of Fingerprints". In: *International Conference on Audio- and Video-Based Biometric Person Authentication* 2091.1629 (2001).

[2] W. Łukasz. "A minutae based matching algorithms in fingerprint recognition". In: *In medical informatics and technologies* 13.6 (2009).
[3] R. W. Anastasia. "Comparing Hilditch, Rosenfeld, Zhang-Suen, and Nagendraprasad Wang Gupta Thinning". In: *World Academy of Science, Engineering and Technology* 78.6 (2011).

[4] P. Mukhopadhyay and B. B. Chaudhuri. "A survey of Hough Transform". In: *Pattern Recognition* 48.1–3 (2015).

[5] V. C. Jane, P. Jianmin, N. R. Shesh, and G. Susan. "ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves". In: *Surgery* 159.6 (2016).

[6] A. Mouad, H. M. Vivek, P. Yannawar, and A. T. Gaikwad. "Overview of Fingerprint Recognition System". In: *International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)* (2016).

[7] D. Chicco. "Siamese Neural Networks: An Overview". In: *Artificial Neural Networks (Methods in Molecular Biology)* 2190 (2020).

[8] G. M. Rashmi, K. Deepti, and G. Rubén. "Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction". In: *Information Sciences* 530 (2020).

[9] J. W. Wong and S. Lai. "Multi-task CNN for restoring corrupted fingerprint images". In: *Pattern Recognition* 101.2 (2020).

[10] K. Abdullah, L. Asif, and A. Shafique. "Machine Learning in Computer Vision: A Review". In: *EAI Endorsed Transactions on Scalable Information Systems* 8.32 (2021).

[11] G. Jochen, H. Fred, M. Dominik, W. Kanit, R. Donghao, N. Rahul, K. Marc, and P. Kayur. "Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels". In: *CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems* 408 (2022).

[12] C. Mahmud and I. Masudul. "Contactless Fingerprint Recognition Using Deep Learning—A Systematic Review". In: *Journal of Cybersecurity and Privacy* 2.1–3 (2022), pages 714–730.

[13] S. Sujana and K. S. v. Reddy. "Multi-modal Biometric System for Face and Fingerprint using Convolutional Neural Network". In: *International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC)* (2022).

[14] *Database (CASIA benchmark)*. http://www.idealtest.org/. Accédé : 2024-01-29.