A fingerprint image captures the unique spatial pattern of ridges and valleys on the human fingertip, serving as a powerful and widely adopted A fingerprint image is a digital representation of the intricate and unique spatial configuration of ridges and valleys found on the human fingertip. These patterns are distinct for every individual and remain virtually unchanged throughout a person’s life, making fingerprints one of the most reliable forms of biometric identification. Their inherent individuality and permanence have made fingerprint recognition systems indispensable across a wide range of applications, including forensic investigations, national identity verification programs, secure access control systems, and personal device authentication.
This chapter, inspired by advancements in biometric sciences,
anthropometry, and computational pattern recognition, investigates refined and
more efficient methods of extracting rich and diverse features from fingerprint
images. The emphasis lies on enhancing the accuracy and reliability of
classification and identification tasks by focusing on extracting ridge information,
minutiae patterns, and Discrete Wavelet Transform (DWT) features. These
advanced features not only facilitate accurate fingerprint matching but also
enable the derivation of soft biometric indicators such as gender, age, and
potentially even blood type. This opens up promising avenues for the
development of lightweight, non-invasive, and cost-effective biometric
classification systems, which are particularly valuable in resource-constrained
settings.
Fingerprint-based systems offer several key advantages over other
biometric modalities, such as iris scans, facial recognition, or voice
analysis. They typically require less storage space, involve simpler data
acquisition procedures, and demand relatively low computational resources.
These attributes make fingerprint recognition an ideal candidate for
large-scale biometric applications, especially in densely populated or
economically limited regions. The methodology explored in this chapter revolves
around an automated framework that systematically analyses the spatial and
structural features of fingerprints. It leverages both spatial domain and
frequency domain analysis techniques to achieve a high-fidelity representation
of fingerprint traits. By focusing on the precise extraction of ridge flows and
minutiae points such as bifurcations and endings, and further enriching this
representation with wavelet-based descriptors, the approach ensures robustness
across various acquisition conditions, including variations in scanner types,
resolutions, lighting conditions, and finger orientation.
Unlike traditional systems that rely solely on features such as
ridge counts, thickness, and basic minutiae, the proposed approach employs
enhanced feature extraction strategies that significantly improve identification
accuracy. Through the application of Discrete Wavelet Transform, fingerprint
images are analysed at multiple resolutions, enabling the capture of both
global and fine-grained local details. This multiresolution capability allows
the system to identify subtle fingerprint variations that might otherwise go
undetected, making the overall classification more precise and resilient.
Further, ridge structure is assessed in terms of quantifiable metrics like
minimum, maximum, and average ridge lengths across the fingerprint. These
measurements add another layer of distinguishing information, especially useful
in scenarios where individuals have similar minutiae layouts but different
ridge formations. In addition to these features, a rich set of minutiae descriptors-such
as the number of ridge bifurcations, ridge endings, and total minutiae
points-is extracted to enhance the discriminatory capability of the system.
By integrating these diverse features-spatial, geometric, and
frequency-based-into a cohesive fingerprint recognition pipeline, this chapter
presents a powerful and holistic approach to automated fingerprint
classification. The comprehensive feature representation facilitates accurate
and efficient matching of test fingerprint samples with stored templates,
making the system suitable for use in high-security environments, law
enforcement databases, and scalable authentication solutions.
In essence, this chapter contributes to the ongoing evolution of
biometric technologies by introducing refined feature extraction techniques
that enhance the reliability and versatility of fingerprint-based systems. The
emphasis on improved accuracy, computational efficiency, and adaptability
underscores the relevance of these techniques in shaping the future of secure and
intelligent biometric identification.
Author(s)
Details
Sayed
Abulhasan Quadri
SECAB Institute of Engineering and Technology, Vijayapura, India.
Chandrakant
P. Divate
SECAB Institute of Engineering and Technology, Vijayapura, India.
Tabasum
Guledgudd
SECAB Institute of Engineering and Technology, Vijayapura, India.
Sayed
Abdulhayan
PACE College, Mangalore, India.
Please see the book here:- https://doi.org/10.9734/bpi/nhstc/v2/5744
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