Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Chiroma, Haruna

doi:10.33969/ais.2021.31010

Cited by 9 publications

(3 citation statements)

References 86 publications

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“…Weber looked into various legal methods for calculating an IoT architecture's privacy and security requirements [23]. A ball can be thrown in the virtual world to authenticate users, according to the work in [24]. They attained a matching accuracy of 92.86 percent in their pilot investigation.…”

Section: Literature Reviewmentioning

confidence: 99%

Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City

Annadurai

Nelson

Devi³

et al. 2022

Energies

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Nowadays, there is a growing demand for information security and security rules all across the world. Intrusion detection (ID) is a critical technique for detecting dangers in a network during data transmission. Artificial Intelligence (AI) methods support the Internet of Things (IoT) and smart cities by creating gadgets replicating intelligent behavior and enabling decision making with little or no human intervention. This research proposes novel technique for secure data transmission and detecting an intruder in a biometric authentication system by feature extraction with classification. Here, an intruder is detected by collecting the biometric database of the smart building based on the IoT. These biometric data are processed for noise removal, smoothening, and normalization. The processed data features are extracted using the kernel-based principal component analysis (KPCA). Then, the processed features are classified using the convolutional VGG−16 Net architecture. Then, the entire network is secured using a deterministic trust transfer protocol (DTTP). The suggested technique’s performance was calculated utilizing several measures, such as the accuracy, f-score, precision, recall, and RMSE. The simulation results revealed that the proposed method provides better intrusion detection outcomes.

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Section: Literature Reviewmentioning

confidence: 99%

Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City

Annadurai

Nelson

Devi³

et al. 2022

Energies

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“…Using biometric information in such a context is referred to as soft biometrics or light biometrics. Soft biometrics has a variety of applications such as gender or age-specifc access control, video retrieval, ofering customized gender or age services, search space reduction in large biometric databases such as in both cloud and noncloud based authentication systems [4][5][6], and forensics use [1-3, 7, 8].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Fingerprint-Based Gender Identification

Berriche

2022

Security and Communication Networks

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Gender identification is a need in forensics investigation. In addition to their use for identification, fingerprints are used for gender identifications. In this work, we propose a model for fingerprint gender classification focusing on a small dataset with imbalanced classes. First, we applied denoising and equalization filters to the fingerprint images. Then, we cropped the image into a region of interest. After preprocessing the fingerprint image, we extracted fast Fourier transform (FFT) and principal component analysis (PCA) features, and we applied the min-max normalization. We train our models with each of them as well as with their fusion. To balance the initially imbalanced datasets, we used two sampling techniques: random sampling and synthetic minority oversampling Technique (SMOTE). Our comparison of the receiver operating characteristic (ROC) of K-nearest neighbours (KNN), Support Vector Machine (SVM), Decision Tree, and forward neural network showed that the best result for the area under the ROC Curve is given by the SVM classifier. Next, we compared the SVM classifier accuracy for each fingerprint position with FFT features, PCA features, and their fusion. In addition, we applied both random hybrid sampling and SMOTE sampling. We applied our method on the National Institute of Standards and Technology (NIST) special database, 4 gray scale images of fingerprint image. We showed that the fusion of FFT and PCA features gives the best gender identification accuracy when the SMOTE dataset balancing technique is applied. We found that the right ring finger was more appropriate with 75.55% of correct male gender identifications and 91.62% female gender identification. Further investigation into dataset balancing techniques will be applied in the future.

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“…Examples include automatically recognizing a person in selfie-images in social media platforms [108], diagnosing diseases from scanned medical images in health care [2], autonomous driving in self-driving cars [110], facial recognition [39] or fingerprint recognition [17] in security industry, automatically adding item details from product images in retail industry [4], and searching images instead of text in visual search engine [142] [87].…”

Section: Problem Statementmentioning

confidence: 99%

Evolving Deep Neural Networks with Explanations for Image Classification

Wang¹

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Image classification problems often face the issues of high dimensionality and large variance within the same class. Deep convolutional neural networks are designed to solve the problem by extracting features using convolutional operations. Researchers have developed complex deep convolutional neural networks to achieve the outstanding performance that outperforms humans. However, the complexity of deep convolutional neural networks brings two side effects. First, the more complex the network architecture is, the harder it is to design. Second, it deteriorates the black-box nature of deep convolutional neural networks, which is harder to explain. To tackle the above two issues, neural architecture search and explainable deep learning have emerged as two promising research areas for automatically designing deep convolutional neural networks and providing explanations of the predictions made by deep learning models, respectively. Evolutionary computation based neural architecture search has been employed to automatically design deep convolutional neural networks that outperform those manually designed, but the computational cost is too high. Surrogate-assisted and transfer learning based methods can be utilised to reduce the computational cost. Furthermore, a branch in explainable deep learning called local approximation does not need machine learning expertise to understand the explanation. The target of local approximation is to find interpretable features. Evolutionary computation has successfully applied to many search problems, but it has never been used in finding local approximation. The overall goal of this thesis is to improve the efficiency of evolutionary neural architecture search in image classification tasks and provide explanations in the inference phase. This can mitigate the major issues -- the difficulty of designing deep convolutional neural networks and the lack of explainability, that significantly affected deep learning being widely used in real-world applications. This thesis proposes a surrogate-assisted particle swarm optimisation (an evolutionary computation algorithm) method to efficiently evolve deep convolutional neural networks. A surrogate model is proposed to predict a better solution from a pair of solutions, and a method for sampling a subset of the dataset as a surrogate dataset is proposed to reduce the computational cost to less than 3 GPU-days, but with very competitive classification accuracies across several benchmark datasets. Next, this thesis proposes to use smaller datasets in multiple source domains to evolve deep convolutional neural networks, and then transfer the learned models to the target domain. This has achieved the goal of accelerating the evolutionary neural architecture search process and improving the generalisation performance, which is supported by the experiment results. Furthermore, this thesis proposes a genetic algorithm (an evolutionary computation algorithm) based method to evolve local explanations to explain the predictions of deep convolutional neural networks. By combining the flexible encoding and the proposed fitness evaluation, the proposed method can efficiently evolve meaning interpretable features in the local explanation. It produces competitive explanations, but 10 times faster than Local Interpretable Model-agnostic Explanations (LIME) -- the state-of-the-art method. Lastly, an evolutionary multi-objective approach is explored to evolve local explanations to reduce the human effort of examining the interpretable features in the local explanations. It adds the second objective of minimising the number of interpretable features, which reduces the human effort in checking them. Additionally, it proposes a method to select only two non-dominated solutions from the pareto front to save the labour cost for end users.

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Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Cited by 9 publications

References 86 publications

Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City

Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City

Comparative Study of Fingerprint-Based Gender Identification

Evolving Deep Neural Networks with Explanations for Image Classification

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