A Novel Technique for Fingerprint Classification based on Naive Bayes Classifier and Support Vector Machine

Mishra, Ashish; Maheshwary, Preeti

doi:10.5120/ijca2017914806

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…A Novel Technique was proposed for Fingerprint Classification based on and Support Vector Machine (SVM) and Naive Bayes Classifier, fingerprint classification is done by the classifier used Naïve Bayes and SVM classifiers by comparing the results of classification, the best results are achieved using Naïve Bayes classifier as compared to SVM classifier. This technique only classifying the fingerprint images and comparing the classification of fingerprints but they did not find the fingerprint verification/recognition rates for both classifiers [7].…”

Section: A Related Workmentioning

confidence: 95%

Performances Enhancement of Fingerprint Recognition System Using Classifiers

et al. 2019

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Fingerprint recognition is best known and generally used as a biometric technology because of their high acceptability, immutability, and uniqueness. A fingerprint consists of ridges and valleys pattern also known as furrows. These patterns fully develop in the mother's womb and remain constant throughout the whole lifetime of the individual. The ridge bifurcation and ridge termination are the main minutiae features that are extracted for identification of individuals in fingerprint recognition system. The aim of this paper is to enhance the performance of the fingerprint recognition systems using classifiers. To achieve the aim, fingerprints from the FV2002 database are used, before these fingerprints are evaluated, image enhancement and binarization is applied as a pre-processing on fingerprints, by combining many methods to build a database of fingerprint features having minutia marking and minutia feature extraction. The fingerprint recognition is presented by image classification using MATLAB classifiers, i.e., Decision Tree, Linear Discriminant Analysis, medium Gaussian support vector machine (MG-SVM), fine K-nearest neighbor, and bagged tree ensemble. The aim of this paper is to make a comparison between classifiers for performance enhancement of the fingerprint recognition system. The MG-SVM classifiers significantly give the highest verification rate of 98.90% among all classifies used.

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Section: A Related Workmentioning

confidence: 95%

Performances Enhancement of Fingerprint Recognition System Using Classifiers

et al. 2019

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“…For comparative purposes, our proposed method accuracy based on the Naïve Bayes (95.90%) outperformed that of Mishra and Maheshwary (2017) where they used the Naïve Bayes and SVM classifier for the classification of fingerprint images and achieved accuracies of 87.4% and 76.06%, respectively. Furthermore, we achieved similar results to that of Baştürk et al (2018).…”

Section: Figure 8: the Confusion Matrix For The Cnn Algorithmmentioning

confidence: 97%

An Investigation of Benford’s Law Divergence and Machine Learning Techniques for Intra-Class Separability of Fingerprint Images

Iorliam

ORGEM

Shehu³

2022

Gazi University Journal of Science Part A: Engineering and Innovation

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Protecting a biometric fingerprint database against attackers is very vital in order to protect against false acceptance rate or false rejection rate. A key property in distinguishing biometric fingerprint images is by exploiting the characteristics of these different types of fingerprint images. The aim of this paper is to perform an intra-class classification of fingerprint images using Benford's law divergence values and machine learning techniques. The usage of these Benford’s law divergence values as features fed into the machine learning techniques has proved to be very effective and efficient in the intra-class classification of biometric fingerprint images. The effectiveness of our proposed methodology was demonstrated on five datasets resulting in a total of 367 samples. All the machine learning techniques used in this experiment were trained using the k-fold cross validation and the dataset was split into ten times (10-folds). The models achieved high intra-class classification mean accuracies of 99.72% for the Convolutional Neural Networks (CNN), and 95.90% for the Naïve Bayes. Again, the Decision Tree and Logistic Regression, achieved accuracies of 95.62%, and 94.47%, respectively. These results showed that Benford’s law features and machine learning techniques, especially the CNN and Naïve Bayes can be effectively applied for the intra-class classification of fingerprint images. The implication of these results is that the different types of fingerprint images can be effectively discriminated using Benford's law divergence values and machine learning technique for forensics and biometrics applications.

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“…Fingerprint gender identification aims to extract gender-related features from an unidentified fingerprint to recognize one's gender information. It can be divided into two stages, namely extracting as well as classifying [1][2][3][4][5][6][7][8], in which the former step is of great significance since the effectiveness of gender identification, is primarily determined by the sufficiency of gender-related features. Nowadays, classifying ridge-related features extracted manually has achieved fairly good results, reaching an overall accuracy for 90% for average [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Research on Gender-related Fingerprint Features, Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification

Qiu

Lin

et al. 2022

Preprint

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Background: Fingerprint is an important biological feature of human body, which contains abundant biometric information.At present, the academic exploration of fingerprint gender characteristics is generally at the level of understanding, and the standardization research is quite limited. Methods: A robust approach is presented in this article, Dense Dilated Convolution ResNet Autoencoder, to extract valid gender information from fingerprints. By replacing the normal convolution operations with the atrous convolution in the backbone, prior knowledge is provided to keep the edge details and the global reception field can be extended. Results: The results were explored from three aspects: 1) The efficiency of the DDC-ResNet. 6 typical automatic feature extraction methods with 9 mainstream classifiers for a total of 54 combinations are evaluated on our dataset and provide unbiased experimental details. Experimental results demonstrate that the combination of our approach outperforms other combinations in terms of average accuracy and separate-gender accuracy. It reaches 96.5% for average and 0.9752(males)/0.9548(females) for separate-gender accuracy. 2) The effect of fingers. It is found that the best performance of classifying gender with separate fingers is achieved by the right ring finger. 3) The effect of specific features. Based on the observations of the concentrations of fingerprints visualized by our approach, it can be inferred that loops and whorls (level 1), bifurcations(level 2), as well as line shapes (level 3) are connected with gender. Finally, we will open source the dataset that contains 6000 fingerprint images. Conclusions: The results are demonstrated that autoencoder networks are a powerful method for extracting gender-specific features to help hide the privacy information of the user’s gender contained in the fingerprint. Our experiments also identified three levels of features in fingerprints that are important for gender differentiation, including loops and whorls shape, bifurcations shape, and line shapes.

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A Novel Technique for Fingerprint Classification based on Naive Bayes Classifier and Support Vector Machine

Cited by 10 publications

References 7 publications

Performances Enhancement of Fingerprint Recognition System Using Classifiers

Performances Enhancement of Fingerprint Recognition System Using Classifiers

An Investigation of Benford’s Law Divergence and Machine Learning Techniques for Intra-Class Separability of Fingerprint Images

Research on Gender-related Fingerprint Features, Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification

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