Using Benford’s Law Divergence and Neural Networks for Classification and Source Identification of Biometric Images

Iorliam, Aamo; Ho, Anthony T. S.; Waller, Adrian; Zhao, Xibin

doi:10.1007/978-3-319-53465-7_7

Cited by 3 publications

(8 citation statements)

References 16 publications

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“…Unfortunately, Newcomb could not prove why the theory and formula worked. Then in 1938, Frank Benford proposed the Benford's law, also referred to as the first digit law, which states that multi-digit numbers beginning with 1, 2, or 3 appear more frequently than multi-digit numbers beginning with 4, 5, 6, 7, 8 and 9 (Benford, 1938;Iorliam et al, 2016). Therefore, original/untampered data is expected to follow Benford's law, which is illustrated in Figure 1.…”

Section: Related Workmentioning

confidence: 99%

“…Since the inception of Benford's law, it is expected that naturally generated datasets should obey this law, whereas tampered or randomly generated datasets should deviate from this law. This inherent characteristic of the Benford's law can lead to important applications in forensics such as detecting anomalies or fraud in a given dataset (Iorliam & Shangbum, 2017;Satapathy et al, 2020) or classifying different types of biometric images (Iorliam et al, 2016;.…”

Section: Figurementioning

confidence: 99%

“…There exist different types of fingerprint images which include contact-less acquired fingerprints, optically acquired fingerprints, and synthetically generated fingerprints (Hildebrandt et al, 2013;Maltoni et al, 2009). Since these fingerprints are used for different purposes, they should not be intentionally or unintentionally used for another purpose as this may cause a serious security threat (Iorliam et al, 2016). Therefore, this paper performs an investigation of machine learning techniques for intra-class classification of fingerprint images to classify fingerprint images of different types that have the same modality.…”

Section: Introductionmentioning

confidence: 99%

“…It has been reported in the literature that since 1938, Benford's law has proved beyond reasonable doubt that it possesses the capability to detect/classify original/untampered data from fake/tampered data (Benford, 1938;Hill, 1998;Iorliam et al, 2016). This interesting law (Benford's law) is therefore adopted for the intra-class separability of fingerprint images.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Biometrics have been gradually replacing traditional methods that recognize people according to what they own, such as cards or keys, or what they know, such as passwords [1]. Iris, face, odor, fingerprint, ear structure, and hand geometry are examples of anatomical characteristics, while voice, walking manner, or signature are behavioral characteristics [2,3]. These biometric modalities should be worldwide, unchangeable, exclusive, and attainable.…”

Section: Introductionmentioning

confidence: 99%

Neutrosophic Rule-Based Identity Verification System Based on Handwritten Dynamic Signature Analysis

Hefny¹,

Hassanien²,

Basha³

2021

Computers, Materials &Amp; Continua

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Identity verification using authenticity evaluation of handwritten signatures is an important issue. There have been several approaches for the verification of signatures using dynamics of the signing process. Most of these approaches extract only global characteristics. With the aim of capturing both dynamic global and local features, this paper introduces a novel model for verifying handwritten dynamic signatures using neutrosophic rule-based verification system (NRVS) and Genetic NRVS (GNRVS) models. The neutrosophic Logic is structured to reflect multiple types of knowledge and relations among all features using three values: truth, indeterminacy, and falsity. These three values are determined by neutrosophic membership functions. The proposed model also is able to deal with all features without the need to select from them. In the GNRVS model, the neutrosophic rules are automatically chosen by Genetic Algorithms. The performance of the proposed system is tested on the MCYT-Signature-100 dataset. In terms of the accuracy, average error rate, false acceptance rate, and false rejection rate, the experimental results indicate that the proposed model has a significant advantage compared to different well-known models.

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Forensics and Biometrics Importance

Iorliam

2018

Fundamental Computing Forensics for Africa

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Using Benford’s Law Divergence and Neural Networks for Classification and Source Identification of Biometric Images

Cited by 3 publications

References 16 publications

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

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

Neutrosophic Rule-Based Identity Verification System Based on Handwritten Dynamic Signature Analysis

Forensics and Biometrics Importance

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