Fingerprints Classification through Image Analysis and Machine Learning Method

Nguyen, Ha Thi; Nguyen, Long

doi:10.3390/a12110241

Cited by 29 publications

(10 citation statements)

References 28 publications

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“…Even, when high end sensors are used for capturing fingerprints, image enhancement is one important pre-processing procedure to facilitate reliable feature extraction. Artificial intelligence and machine learning though used in fingerprint recognition systems, image quality and features determine the efficiency of these algorithms [13]. In order to address relevant issues, the work in this study proposes a method for enhancement, feature extraction and template creation.…”

Section: Problem Definitionmentioning

confidence: 99%

Effective minutiae extraction and template creation in fingerprints

Karuppathal¹,

Kalpana²,

Thilagavathy³

et al. 2022

J. Phys.: Conf. Ser.

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Despite years of research in using the fingerprint as a biometric entity, there remains a niche to improve the underlying accuracy, specifically on enhancement, feature extraction and classification consideration. Attempted in this paper is a ridge compensation filtering technique for enhancement, followed by minutiae extraction and false minutiae removal and eventually a procedure for template creation. The extracted features are evaluated using metrics like accuracy, error rate and goodness index. These values were found to be better when compared with the results of some state-of-art algorithms, which endorse the effectiveness of the first two stages. Mahalanobis distance (MD) is used to recognise the fingerprints through the generated features that resulted in accuracy above 92%. Finally, convolution neural network (CNN) is used to cross-examine the recognition capacity. Though CNN also able to give a similar accuracy, the proposed algorithm is simple and robust and hence easily embedded in hand-held devices.

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Section: Problem Definitionmentioning

confidence: 99%

Effective minutiae extraction and template creation in fingerprints

Karuppathal¹,

Kalpana²,

Thilagavathy³

et al. 2022

J. Phys.: Conf. Ser.

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show abstract

“…In contrast, ML, deep learning (DL), and AI excel at automatic pattern recognition from large amounts of biomedical image data. In particular, machine learning and deep learning algorithms (e.g., support vector machine, neural network, and convolutional neural network) have achieved impressive results in biomedical image classification [14][15][16][17][18][19][20][21][22][23]. Classification helps to organize biomedical image databases into image categories before diagnostics [24][25][26][27][28][29][30].…”

Section: A Survey Of Biomedical Imagementioning

confidence: 99%

Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms

Tchapga

Mih

Kouanou

et al. 2021

Journal of Healthcare Engineering

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In modern-day medicine, medical imaging has undergone immense advancements and can capture several biomedical images from patients. In the wake of this, to assist medical specialists, these images can be used and trained in an intelligent system in order to aid the determination of the different diseases that can be identified from analyzing these images. Classification plays an important role in this regard; it enhances the grouping of these images into categories of diseases and optimizes the next step of a computer-aided diagnosis system. The concept of classification in machine learning deals with the problem of identifying to which set of categories a new population belongs. When category membership is known, the classification is done on the basis of a training set of data containing observations. The goal of this paper is to perform a survey of classification algorithms for biomedical images. The paper then describes how these algorithms can be applied to a big data architecture by using the Spark framework. This paper further proposes the classification workflow based on the observed optimal algorithms, Support Vector Machine and Deep Learning as drawn from the literature. The algorithm for the feature extraction step during the classification process is presented and can be customized in all other steps of the proposed classification workflow.

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“…e classifier utilizes K(K − 1)/2 binary SVM models using the one-versus-one coding design, with K being the number of different class labels. e SVM algorithm is mainly used for locating a hyperplane that precisely groups the associated feature points into classes in an N-dimensional space with N features [21]. Sets of data points that occur on either side of a hyperplane can be banded together into separate classes.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

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

Appati

Nartey

Owusu

et al. 2021

International Journal of Mathematics and Mathematical Sciences

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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.

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Fingerprints Classification through Image Analysis and Machine Learning Method

Cited by 29 publications

References 28 publications

Effective minutiae extraction and template creation in fingerprints

Effective minutiae extraction and template creation in fingerprints

Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms

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

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