Entropy based Local Binary Pattern (ELBP) feature extraction technique of multimodal biometrics as defence mechanism for cloud storage

Vidya, B. Sree; Chandra, E.

doi:10.1016/j.aej.2018.12.008

Cited by 39 publications

(7 citation statements)

References 19 publications

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“…The second method for comparison is the Jagadiswary et al 12 approach of the fusion level multimodal combination approach, which achieves a higher accuracy of 87.6%. Vidya & Chandrause 13 ELBP method feature extraction achieved higher accuracy in multimodal biometrics of 91.0%. The next method of comparison is Yang et al 14 feature-level matching, given a higher accuracy of 90%.…”

Section: • Equal Error Rate (Eer)mentioning

confidence: 98%

“…The experimental analysis revealed that they have enhanced performance in terms of GAR of approximately 95.3% and FAR of approximately 0.01%. Vidya & Chandra 13 present a novel security model for cloud-based encrypted storage that uses times of high-demand authentication by combining different biometric modalities from individuals and granting or denying access based on the results. ELBP is a modern texture-based dimensionality reduction technique that is proposed to represent entropy details in one dimension using a local binary pattern feature vector.…”

Section: Related Workmentioning

confidence: 99%

“…The accuracy comparison is depicted in Fig. 6 (A = ref 11 ; B = ref 12 ; C = ref 13 ; D = ref 14 ; E = ref 10 ; F = ref 21 ; G = Proposed OFGA method). Similarly, the proposed approach is compared for EER with the state-of-the-art approaches.…”

Section: • Equal Error Rate (Eer)mentioning

confidence: 99%

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Enhanced multimodal biometric recognition approach for smart cities based on an optimized fuzzy genetic algorithm

Rajasekar¹,

Predić

Saračević

et al. 2022

Sci Rep

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Biometric security is a major emerging concern in the field of data security. In recent years, research initiatives in the field of biometrics have grown at an exponential rate. The multimodal biometric technique with enhanced accuracy and recognition rate for smart cities is still a challenging issue. This paper proposes an enhanced multimodal biometric technique for a smart city that is based on score-level fusion. Specifically, the proposed approach provides a solution to the existing challenges by providing a multimodal fusion technique with an optimized fuzzy genetic algorithm providing enhanced performance. Experiments with different biometric environments reveal significant improvements over existing strategies. The result analysis shows that the proposed approach provides better performance in terms of the false acceptance rate, false rejection rate, equal error rate, precision, recall, and accuracy. The proposed scheme provides a higher accuracy rate of 99.88% and a lower equal error rate of 0.18%. The vital part of this approach is the inclusion of a fuzzy strategy with soft computing techniques known as an optimized fuzzy genetic algorithm.

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Section: • Equal Error Rate (Eer)mentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

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Enhanced multimodal biometric recognition approach for smart cities based on an optimized fuzzy genetic algorithm

Rajasekar¹,

Predić

Saračević

et al. 2022

Sci Rep

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“…Input ( ) The HELBP 33 feature considers the pattern information over spatial distribution. This feature is mainly employed to capture the high information content of image textures.…”

Section: Haralick Featuresmentioning

confidence: 99%

Alzheimer's disease classification using competitive swarm multi‐verse optimizer‐based deep neuro‐fuzzy network

Allada

Bhavani

Chaduvula³

et al. 2023

Concurrency and Computation

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SummaryClassification of Alzheimer's disease (AD) from neuroimaging, like magnetic resonance imaging (MRI) through deep learning classifier has been increasing in research in recent decades. However, it is required for enhancing the accuracy of the AD classification for effective treatment. In this work, an efficient model termed competitive swarm multi‐verse optimizer + deep neuro‐fuzzy network (CSMVO + DNFN) is designed to accurately classify stages of AD. Preprocessing is done with a median filter. Then, the resulting image is segmented to find the interested regions by channel‐wise feature pyramid network module (CFPNet‐M). Some features obtained from the segmented image are haralick, convolutional neural network, and texture features. The devised method is more efficient in classifying different stages of AD with MRI modality. Furthermore, the developed model attained higher performance using metrics like the accuracy of 89.9%, sensitivity of 89.6%, and specificity of 87.0% based on the k‐fold value.

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“…Supervised methods involve training a classifier with a dataset (training set) to differentiate between vessel and non-vessel pixels, further classified into machine learning and deep learning algorithms. Machine learning approaches typically involve feature extraction, selection, and classification stages, with various feature extractors and classifiers PSO-HRVSO for medical image classification, including bag-of-visual-words, Gaussian filter, and Gabor filter, along with classifiers like K-Nearest Neighbors (K-NN), Random Forest, Support Vector Machine (SVM), and Artificial Neural Networks (ANN) [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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2024

SIPIJ

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The structure of retinal blood vessels is crucial for the early detection of diabetic retinopathy, a leading cause of blindness worldwide. Yet, accurately segmenting retinal vessels poses significant challenges due to the low contrast and noise present in capillaries.The automated segmentation of retinal blood vessels significantly enhances Computer-Aided Diagnosis for diverse ophthalmic and cardiovascular conditions. It is imperative to develop a method capable of segmenting both thin and thick retinal vessels to facilitate medical analysis and disease diagnosis effectively. This article introduces a novel methodology for robust vessel segmentation, addressing prevalent challenges identified in existing literature.The methodology PSO-HRVSO comprises three key stages: pre-processing, main processing, and postprocessing. In the initial stage, filters are employed for image smoothing and enhancement, leveraging PSO optimization. The main processing phase is bifurcated into two configurations. Initially, thick vessels are segmented utilizing an optimized top-hat approach, homo-morphic filtering, and median filter. Subsequently, the second configuration targets thin vessel segmentation, employing the optimized top-hat method, homomorphic filtering, and matched filter. Lastly, morphological image operations are conducted during the post-processing stage.The PSO-HRVSO method underwent evaluation using two publicly accessible databases (DRIVE and STARE), measuring performance across three key metrics: specificity, sensitivity, and accuracy. Analysis of the outcomes revealed averages of 0.9891, 0.8577, and 0.0.9852 for the DRIVE dataset, and 0.9868, 0.8576, and 0.9831 for the STARE dataset, respectively.The PSO-HRVSO technique yields numerical results that demonstrate competitive average values when compared to current methods. Moreover, it sur-passes all leading unsupervised methods in terms of specificity and accuracy. Additionally, it outperforms the majority of state-of-the-art supervised methods without incurring the computational costs associated with such algorithms. Detailed visual analysis reveals that the PSO-HRVSO approach enables a more precise segmentation of thin vessels compared to alternative procedures.

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Entropy based Local Binary Pattern (ELBP) feature extraction technique of multimodal biometrics as defence mechanism for cloud storage

Cited by 39 publications

References 19 publications

Enhanced multimodal biometric recognition approach for smart cities based on an optimized fuzzy genetic algorithm

Enhanced multimodal biometric recognition approach for smart cities based on an optimized fuzzy genetic algorithm

Alzheimer's disease classification using competitive swarm multi‐verse optimizer‐based deep neuro‐fuzzy network

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