Report Project Fingerprint Authentication Course Biometric Authentication Systems.pdf

• Forensic Significance: Fingerprint ridge patterns play a crucial role in forensic investiga-tions, where the analysis of latent prints and the identification of minutiae points within

School of Information and Communication Technology

Report

Project: Fingerprint Authentication

Course: Biometric Authentication Systems

Group 4:

Nguyen Quang Hung (20214961)

Nguyen Tuan Long (20214963)

Do Quang Minh (20210579)

Instructors:

Dr Tran Nguyen Ngoc

Dr Ngo Thanh Trung

1.1 Problem statement 3

1.2 Summary of the fields 3

1.3 Motivation of our study 3

2 Method details 4 2.1 Basic theory of fingerprint recognition 4

2.1.1 Ridge Patterns 4

2.1.2 Minutiae Points 5

2.1.3 Fingerprint Imaging 6

2.2 Data Preprocessing 7

2.2.1 Normalization 7

2.2.2 Oriented Field Estimation 8

2.2.3 Ridge Frequency Estimation 9

2.2.4 Gabor Filter 10

2.3 Recognition Algorithm 10

2.3.1 Introduction to ORB 10

2.3.2 Adaptability to Fingerprint Recognition 12

2.3.3 Fast and Efficient Matching 12

3 Experiment 13 3.1 Data 13

3.2 Data Preprocessing 13

3.3 Recognition Method 14

3.4 Result 17

Page 1

Group contribution:

• ORB algortihm, Reports: Nguyen Tuan Long

• Data Preprocessing, Slides: Do Quang Minh

• Presentation: Nguyen Quang Hung

1 Introduction

1.1 Problem statement

Fingerprint authentication encounters challenges in achieving high accuracy, particularly in scenarios with low-quality prints and variations in skin conditions, necessitating improved matching algorithms Striking a balance between false acceptance and false rejection rates

is critical for the reliability and effectiveness of the authentication system Robustness is essential to handle variations in fingerprints due to factors like aging, injuries, and different sensor types, highlighting the need for adaptable solutions Security concerns arise from the susceptibility to spoofing attacks, emphasizing the importance of implementing advanced anti-spoofing techniques to enhance system security To ensure widespread acceptance, addressing these challenges requires advancements in technology, including faster matching speeds and streamlined user enrollment processes

1.2 Summary of the fields

Fingerprint authentication encounters multifaceted challenges spanning accuracy, false accep-tance and rejection rates, robustness, security, template protection, speed, user enrollment processes, and interoperability Achieving high accuracy is imperative, particularly in dealing with low-quality prints and variations in skin conditions, necessitating the development of advanced matching algorithms Balancing false acceptance and rejection rates is crucial for the system’s reliability, ensuring accurate identification while minimizing the risk of unautho-rized access The technology must exhibit robustness to variations in fingerprints caused by factors such as aging, injuries, and diverse sensor types, emphasizing the need for adaptable and resilient solutions Security vulnerabilities arise from the susceptibility to spoofing at-tacks, demanding the implementation of sophisticated anti-spoofing techniques to fortify the system against fraudulent attempts Secure template protection mechanisms are essential to preserve the privacy and integrity of stored fingerprint data, preventing unauthorized access and breaches Enhancing matching speed without compromising accuracy is vital for deliver-ing a seamless and efficient user experience Streamlindeliver-ing user enrollment processes is equally critical for widespread adoption, ensuring user-friendly and secure registration of fingerprints The challenges of interoperability and standardization further underscore the need for com-mon industry standards to facilitate seamless integration across diverse fingerprint recognition systems and applications

1.3 Motivation of our study

The primary objective of our project is to build and implement a fingerprint recognition sys-tem This system will apply a machine learning methodology: Gabor Filter for data prepro-cessing and Oriented FAST and Rotated BRIEF (ORB) algorithm for recognition method.The main goal of this project is to develop a system that will be able to recognize whether 2 fin-gerprints come from the same person or not For this purpose, the images are first collected from a public data set Then digital imaging techniques are applied to the same images in order to improve their quality Once the image is reprocessed, the so-called image is searched critical points that are later compared according to their Hamming distance

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2 Method details

2.1 Basic theory of fingerprint recognition

2.1.1 Ridge Patterns

Figure 1: Three basic fingerprint ridge patterns

• Distinctive Patterns: Fingerprint ridge patterns, comprising loops, arches, and whorls, are unique to each individual and serve as a fundamental basis for fingerprint recognition

• Individual Variation: The specific arrangement and combination of ridge patterns exhibit considerable individual variation, contributing to the statistical rarity and uniqueness of each person’s fingerprints

• Forensic Significance: Fingerprint ridge patterns play a crucial role in forensic investiga-tions, where the analysis of latent prints and the identification of minutiae points within these patterns aid in linking individuals to crime scenes

2.1.2 Minutiae Points

Figure 2: Minutiae based extraction in fingerprint

• Definition and Types: Minutiae points are specific features in fingerprints, including ridge endings and bifurcations, representing unique locations where ridges end, split, or converge

• Individual Variation: The arrangement and distribution of minutiae points are highly individualized, contributing to the uniqueness of each person’s fingerprint and forming the basis for reliable biometric identification

• Fingerprint Template Creation: Minutiae points are crucial for creating a condensed digital fingerprint template used in matching algorithms These templates capture essential features for accurate identification

• Forensic and Security Applications: Minutiae points play a pivotal role in forensic inves-tigations, aiding in the comparison of latent prints from crime scenes with known fingerprints They are also considered in anti-spoofing measures to enhance security in fingerprint recog-nition systems

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2.1.3 Fingerprint Imaging

Figure 3: Fingerprint imaging procedure

• Capture Methods: Fingerprint imaging involves various capture methods, such as optical, capacitive, and ultrasonic sensors These methods detect and record the unique patterns of ridges and valleys on the fingertip

• Valuable Data: During imaging, both the raised ridges and indented valleys on the skin’s surface are captured This comprehensive data is essential for creating accurate and detailed representations of the fingerprint

• Enhancement Techniques: Preprocessing techniques, including image enhancement, are applied to improve the clarity and quality of captured fingerprint images These enhancements contribute to more precise feature extraction and analysis

• Applications: Fingerprint imaging is used in various applications, including access control systems, mobile devices, forensic investigations, and identity verification The captured images are processed to create digital templates used for matching and identification

2.2 Data Preprocessing

2.2.1 Normalization

Figure 4: Fingerprint imaging procedure

The enhancement in fingerprint image remains as a vital step to recognize or verify the identity

of person The noise is influenced during the acquisition of fingerprint image The poor quality images are captured and leads to inaccurate levels of discrepancy in values of gray level beside the ridges and furrows because of non-uniformity of ink and contact of finger on scanner This poor quality images are affect to the minutiae extraction algorithm which may extract incorrect minutiae and affect to the fingerprint matching during post-processing Normalization is the preprocessing step for increase the quality of images by removing the noise and alters the range of pixel intensity values The mean and variance are used in process to reduce variants in gray-level values along ridges and valleys

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2.2.2 Oriented Field Estimation

Figure 5: Fingerprint - Discrete orientation field - Orientation field estimated by mean square

method

Fingerprint orientation field estimation is a critical preprocessing step in fingerprint recognition, aiming to determine the local directionality of ridges Using methods such as Gabor filtering, it analyzes small, overlapping regions of the fingerprint image to provide a localized and adaptive estimation of ridge orientation The result is an orientation map that visually represents the local ridge orientations across the entire fingerprint This orientation field is essential for subsequent processes, including ridge frequency analysis, minutiae extraction, and overall enhancement of ridge structures The accuracy of orientation field estimation directly influences the precision of minutiae extraction, a key factor in fingerprint recognition systems Overall, fingerprint orientation field estimation enhances the visibility of ridge patterns and contributes significantly to the accuracy and reliability of fingerprint recognition

2.2.3 Ridge Frequency Estimation

Figure 6: Ridge Frequency

Ridge frequency estimation is a crucial step in fingerprint image processing, focused on determining the frequency of ridge patterns within a fingerprint Utilizing techniques like Fourier analysis, it involves analyzing the variations in the ridge spacings across different regions of the fingerprint image The resulting ridge frequency map provides a visual representation of the local frequencies, aiding in subsequent analysis and enhancement Accurate ridge frequency estimation is vital for tasks such as fingerprint normalization, orientation field correction, and overall improvement of fin-gerprint recognition system performance It enhances the precision of feature extraction processes, contributing to the reliability and effectiveness of fingerprint identification

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2.2.4 Gabor Filter

Figure 7: Gabor filter responses and binarized images according to the degree of orientation error

Fingerprint Gabor filtering is a powerful image processing technique used for enhancing the visibility

of ridge patterns in fingerprint images Gabor filters, inspired by mathematical functions, are employed to capture specific frequency and orientation components of ridge structures The filters are convolved with the fingerprint image, emphasizing ridge features while suppressing noise and irrelevant details This process aids in creating a clearer representation of the fingerprint, essential for subsequent analysis and recognition tasks Gabor filtering is particularly effective in capturing fine details and textures in fingerprint images, contributing to accurate feature extraction and matching algorithms Its adaptability to different frequencies and orientations makes it a valuable tool for improving the overall quality and discriminative power of fingerprint recognition systems

2.3 Recognition Algorithm

2.3.1 Introduction to ORB

• FAST Keypoint Detection: ORB begins by employing the FAST algorithm for the rapid identification of keypoints in an image FAST identifies points where pixel intensities differ significantly from their neighbors, providing a set of keypoints for further analysis

• BRIEF Descriptor Generation: After keypoint detection, the BRIEF algorithm is utilized

to generate binary descriptors for the keypoints BRIEF creates binary sequences by sampling pairs of pixel intensities and assigning binary values based on their relative magnitudes

• Rotation Invariance: ORB introduces rotation invariance by computing the dominant ori-entation for each keypoint The binary descriptors are then rotated based on this oriori-entation, ensuring consistent matching even when the images are rotated

• Efficient Hamming Distance Matching: ORB employs the Hamming distance for effi-cient binary descriptor matching The Hamming distance measures the dissimilarity between two binary strings by counting the differing bits This approach significantly enhances com-putational efficiency compared to traditional methods using Euclidean distance

• Scale Invariance: While ORB primarily focuses on rotation and scale invariance, it also incorporates scale invariance to some extent, making it suitable for applications where the scale of features might vary

• Applications: ORB is widely used in computer vision tasks such as object recognition, image stitching, and visual odometry Its computational efficiency makes it particularly valuable in real-time applications, including robotics and augmented reality

The ORB algorithm’s ability to efficiently detect and describe features, along with its speed and robustness, has contributed to its popularity in various fields requiring rapid and reliable image processing Its balance between accuracy and computational efficiency makes it well-suited for real-world applications in both industry and academia

Figure 8: ORB algorithm feature

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2.3.2 Adaptability to Fingerprint Recognition

• Texture and Structure: Fingerprint recognition heavily relies on the unique patterns and structures of ridges and valleys While ORB is effective in capturing distinctive features in textured images, the specific requirements of fingerprint images may necessitate algorithms designed specifically for ridge-based patterns

• Rotation and Scale Invariance: While ORB introduces rotation invariance by computing dominant orientations, fingerprint recognition often requires more advanced methods to handle the complex, non-linear deformations that can occur in fingerprint images Achieving robust rotation and scale invariance in fingerprint recognition may require additional adaptations

• Binary Descriptors: ORB uses binary descriptors, making it computationally efficient However, the binary nature might not capture the continuous and nuanced variations in ridge patterns observed in fingerprint images Fingerprint recognition systems often employ minutiae points and other specialized features

• Adaptations Needed: Adapting ORB for fingerprint recognition might involve additional processing steps and feature extraction methods specific to fingerprint patterns This could include algorithms tailored for ridge orientation field estimation, minutiae extraction, and handling complex ridge structures

• Specialized Fingerprint Algorithms: Fingerprint recognition systems often rely on spe-cialized algorithms designed explicitly for the unique characteristics of fingerprint images These algorithms may include techniques such as ridge frequency analysis, local ridge orien-tation estimation, and advanced minutiae matching

In summary, while ORB is a versatile and efficient algorithm for general-purpose computer vision tasks, fingerprint recognition demands specialized approaches Researchers and practitioners often prefer algorithms specifically designed for fingerprint analysis, taking into account the unique char-acteristics and challenges posed by fingerprint images These specialized algorithms provide better accuracy and reliability in the context of fingerprint recognition systems

2.3.3 Fast and Efficient Matching

The ORB (Oriented FAST and Rotated BRIEF) algorithm is known for its fast and efficient match-ing capabilities, makmatch-ing it suitable for real-time computer vision applications Here are key aspects that contribute to the fast and efficient matching of ORB:

• Real-Time Applications: ORB’s computational efficiency makes it well-suited for real-time applications such as object recognition, tracking, and augmented reality Its ability to perform