A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks

Barzut, Srđan; Milosavljević, Milan; Adamović, Saša; Saračević, Muzafer; Maček, Nemanja; Gnjatović, Milan

doi:10.3390/math9070730

Cited by 13 publications

(11 citation statements)

References 25 publications

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“…biometrics and hard (facial expression, fingerprint, palm print-based geometry, etc.) biometrics [ 37 ]. Soft biometrics refers to features that change frequently depending on the situation.…”

Section: Related Workmentioning

confidence: 99%

Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN

Kumar

Saraswat

Ather

et al. 2022

Computational Intelligence and Neuroscience

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Signature verification is the widely used biometric verification method for maintaining individual privacy. It is generally used in legal documents and in financial transactions. A vast range of research has been done so far to tackle different system issues, but there are various hot issues that remain unaddressed. The scale and orientation of the signatures are some issues to address, and the deformation of the signature within the genuine examples is the most critical for the verification system. The extent of this deformation is the basis for verifying a given sample as a genuine or forgery signature, but in the case of only a single signature sample for a class, the intra-class variation is not available for decision-making, making the task difficult. Besides this, most real-world signature verification repositories have only one genuine sample, and the verification system is abiding to verify the query signature with a single target sample. In this work, we utilize a two-phase system requiring only one target signature image to verify a query signature image. It takes care of the target signature’s scaling, orientation, and spatial translation in the first phase. It creates a transformed signature image utilizing the affine transformation matrix predicted by a deep neural network. The second phase uses this transformed sample image and verifies the given sample as the target signature with the help of another deep neural network. The GPDS synthetic and MCYT datasets are used for the experimental analysis. The performance analysis of the proposed method is carried out on FAR, FRR, and AER measures. The proposed method obtained leading performance with 3.56 average error rate (AER) on GPDS synthetic, 4.15 AER on CEDAR, and 3.51 AER on MCYT-75 datasets.

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Section: Related Workmentioning

confidence: 99%

Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN

Kumar

Saraswat

Ather

et al. 2022

Computational Intelligence and Neuroscience

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“…Where N i = number of input neurons, N o = number of output neurons, N s = number of samples in training data set, α = an arbitrary scaling factor usually be in the range [2,10] and N t = the number order for hidden layer.…”

Section: Plos Onementioning

confidence: 99%

An improved long short term memory network for intrusion detection

Awad,

Ali,

Gaber

2023

PLoS ONE

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Over the years, intrusion detection system has played a crucial role in network security by discovering attacks from network traffics and generating an alarm signal to be sent to the security team. Machine learning methods, e.g., Support Vector Machine, K Nearest Neighbour, have been used in building intrusion detection systems but such systems still suffer from low accuracy and high false alarm rate. Deep learning models (e.g., Long Short-Term Memory, LSTM) have been employed in designing intrusion detection systems to address this issue. However, LSTM needs a high number of iterations to achieve high performance. In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. The performance of ILSTM and the intrusion detection system were evaluated using two public datasets (NSL-KDD dataset and LITNET-2020) under nine performance metrics. The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. The ILSTM achieved an accuracy of 93.09% and a precision of 96.86% while LSTM gave an accuracy of 82.74% and a precision of 76.49%. Also, the ILSTM performed better than LSTM in both datasets. In addition, the statistical analysis showed that ILSTM is more statistically significant than LSTM. Further, the proposed ISTLM gave better results of multiclassification of intrusion types such as DoS, Prob, and U2R attacks.

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“…Similarly, the backward LSTM evaluates the hidden state vector hb t using x t and hb t−1 from the reverse direction. en the terminal hidden state vector of the BiLSTM is obtained by combining the forward and backward hidden vector (hf t and hb t ) as shown in equation (6).…”

Section: Bilstm Layermentioning

confidence: 99%

“…Image caption generation is a tedious process of identifying various objects present in the image and the association between those objects, attributes, and behavior. e advancement of deep learning techniques in various applications [3][4][5][6] has a wider scope in caption generation models.…”

Section: Introductionmentioning

confidence: 99%

Caption Generation Based on Emotions Using CSPDenseNet and BiLSTM with Self-Attention

S¹,

K²,

Kaliappan

et al. 2022

Applied Computational Intelligence and Soft Computing

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Automatic image caption generation is an intricate task of describing an image in natural language by gaining insights present in an image. Featuring facial expressions in the conventional image captioning system brings out new prospects to generate pertinent descriptions, revealing the emotional aspects of the image. e proposed work encapsulates the facial emotional features to produce more expressive captions similar to human-annotated ones with the help of Cross Stage Partial Dense Network (CSPDenseNet) and Self-attentive Bidirectional Long Short-Term Memory (BiLSTM) network. e encoding unit captures the facial expressions and dense image features using a Facial Expression Recognition (FER) model and CSPDense neural network, respectively. Further, the word embedding vectors of the ground truth image captions are created and learned using the Word2Vec embedding technique. en, the extracted image feature vectors and word vectors are fused to form an encoding vector representing the rich image content. e decoding unit employs a self-attention mechanism encompassed with BiLSTM to create more descriptive and relevant captions in natural language. e Flickr11k dataset, a subset of the Flickr30k dataset is used to train, test, and evaluate the present model based on ve benchmark image captioning metrics. ey are BiLingual Evaluation Understudy (BLEU), Metric for Evaluation of Translation with Explicit Ordering (METEOR), Recall-Oriented Understudy for Gisting Evaluation (ROGUE), Consensus-based Image Description Evaluation (CIDEr), and Semantic Propositional Image Caption Evaluation (SPICE). e experimental analysis indicates that the proposed model enhances the quality of captions with 0.6012(BLEU-1), 0.3992(BLEU-2), 0.2703(BLEU-3), 0.1921(BLEU-4), 0.1932(METEOR), 0.2617(CIDEr), 0.4793(ROUGE-L), and 0.1260(SPICE) scores, respectively, using additive emotional characteristics and behavioral components of the objects present in the image.

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A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks

Cited by 13 publications

References 25 publications

Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN

Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN

An improved long short term memory network for intrusion detection

Caption Generation Based on Emotions Using CSPDenseNet and BiLSTM with Self-Attention

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