Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition

Justice Kwame Appati,Ebenezer Owusu,Ismail Wafaa Denwar,Prince Kofi Nartey,Chin-Chia Wu

doi:10.1155/2021/5545488

Justice Kwame Appati, Ebenezer Owusu + Show 3 more

Open Access

https://doi.org/10.1155/2021/5545488

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Abstract

Biometrics consists of scientific methods of using a person’s unique physiological or behavioral traits for electronic identification and verification. The traits for biometric identification are fingerprint, voice, face, and palm print recognition. However, this study considers fingerprint recognition for in-person identification since they are distinctive, reliable, and relatively easy to acquire. Despite the many works done, the problem of accuracy still persists which perhaps can be attributed to the varying characteristic of the acquisition devices. This study seeks to improve the issue recognition accuracy with the proposal of the fusion of a two transform and minutiae models. In this study, a transform-minutiae fusion-based model for fingerprint recognition is proposed. The first transform technique, thus wave atom transform, was used for data smoothing while the second transform, thus wavelet, was used for feature extraction. These features were added to the minutiae features for person recognition. Evaluating the proposed design on the FVC 2002 dataset showed a relatively better performance compared to existing methods with an accuracy measure of 100% as to 96.67% and 98.55% of the existing methods.

Highlights

Biometrics deals with the technology used for electronic identification and verification of an individual based on behavioral and physiological characteristics they possess [1]
Biometric devices are conventionally made of a biometric engine
For DB3_B, the model’s performance was poor with the minutiae features with an accuracy score of 40% but performed better on the DWT features yielding 95% accuracy. e performance for DB4_B in contrast to the initial datasets yielded a higher percentage accuracy for the minutiae features, with 65% compared to a relatively low accuracy of 40% for the DWT

Summary

Introduction

Biometrics deals with the technology used for electronic identification and verification of an individual based on behavioral and physiological characteristics they possess [1]. 2. Research Design is section discusses the fingerprint image preprocessing and how these fingerprint characteristics are extracted to aid in person recognition. Is section discusses the various stages of smoothing fingerprint images to help extract relevant features for accurate recognition. Step 3: analyze the thinned fingerprint image and detect the minutiae by using the 8-neighborhood pixels to compute for each block of the ridge bifurcations and ridge endings. Characteristics images to extract these coefficients for the recognition of the fingerprint Once both minutiae and db wavelet coefficients. E Daubechies 9 (db9) wavelet is considered in this study as it generates similar results compared to complex Gabor wavelets [18] It extracts more appropriate features from an image relative to simpler wavelets such as Haar. (2) continuous wavelet transforms return arrays that are a single dimension larger than their input data

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