Score level fusion of major and minor finger knuckle patterns based symmetric sum-based rules for person authentication

Hammouche, Rabah; Attia, Abdelouahab; Akhrouf, Samir

doi:10.1007/s12530-022-09430-8

Cited by 6 publications

(4 citation statements)

References 40 publications

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“…Hammouche et al 20 suggested using the Cosine Mahalanobis distance in the matching stage. Additionally, a score‐level fusion based on many biometric systems has been recommended.…”

Section: Review Of Related Workmentioning

confidence: 99%

Secure personal authentication in fog devices via multimodal rank‐level fusion

Selvaraj

Thangavel

2023

Concurrency and Computation

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Summary In recent years, Fog and edge computing have gained enormous increases and usage of Internet of Things (IoT) devices in research and development even though it provides challenges such as security and privacy. One way to ensure this is a fusion of multimodal biometrics authentication systems, which offer highly reliable biometric authentication solutions to reduce the failure to enroll rate, increase the degree of freedom, and deter spoof attacks. Since unimodal biometric systems often face significant limitations due to sensitivity to noise, data quality, non‐universality, and illumination variations. In this research, we propose management of access control to ensure the desired level of security, a Rank level fusion integration method. ORL, GT, CASIA, and VGG‐face databases were used for testing and evaluating the implemented system. Thus, using the LDA with SCE based feature selection approach, the average GAR percent for the facial, hearing, and palm vein is 3.25% for the facial and muffs pulse and 8.55% for the head and palm vein result was the overall performance of the biometric system even in the presence of low‐quality data in the Fog environment.

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“…Hammouche et al 20 suggested using the Cosine Mahalanobis distance in the matching stage. Additionally, a score‐level fusion based on many biometric systems has been recommended.…”

Section: Review Of Related Workmentioning

confidence: 99%

Secure personal authentication in fog devices via multimodal rank‐level fusion

Selvaraj

Thangavel

2023

Concurrency and Computation

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“…The suggested approach in this study [13] began by separating two components: light and reflectance. First, the adaptive single-scale retinex (ASSR) technique was used to compute these components from the major or minor ROI image.…”

Section: Literaturementioning

confidence: 99%

“…Then, using Independent Component Analysis (ICA), binarized statistical image features descriptor (BSIF) trained an ensemble of convolution filters from the original images. Also, the major and minor pixels taken from dorsal finger images and reflectance and illumination images were coded using these learnt filters [13]. Finally, the fusion technique was based on the minor and major dorsal finger scores level fusion [13].…”

Section: Literaturementioning

confidence: 99%

“…Also, the major and minor pixels taken from dorsal finger images and reflectance and illumination images were coded using these learnt filters [13]. Finally, the fusion technique was based on the minor and major dorsal finger scores level fusion [13]. However, there is a lack of studies investigating the fusion of the dorsal hands from red-green-blue (RGB) images of all fingernails, knuckle creases, and whole hands.…”

Section: Literaturementioning

confidence: 99%

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A Multi-modal Biometric Approach Based on Score-level Fusion and Fine-tuning Deep Learning Features

Alghamdi

2022

2022 IEEE 11th International Conference on Intelligent Systems (IS)

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This paper presents a multimodal biometric approach applied to all fingernails and knuckle creases of the five human fingers for identifying persons. In this paper, the proposed biometric technique consists of several phases. The method starts with the detection and localisation of the main components of the hand, defining the region of interest (ROI), segmentation, feature extraction by retraining the DenseNet201 model, measuring the similarity using different metrics, and lastly, improving the person identification performance by implementing score-level fusion. This approach presents different methods for person identification, which combine fingernails, knuckles based on the modality type, and whole hands based on different similarity metrics. This paper uses various similarity metrics to distinguish between individuals. These include the Bray-Curtis, Cosine, and Euclidean metrics. Two main score-level fusion techniques are employed: the majority voting (MV) and weighted average (WA). The experimental results are evaluated with well-known databases, the '11k Hands' and the Hong Kong Polytechnic University Contactless Hand Dorsal Images 'PolyU', show the proposed algorithm's efficiency. Using the MV on the Bray-Curtis similarity measure, the fingernail-based and the base-knucklebased fusion obtained 100% in the identification estimation. In addition, the identification rate gained 100% in regions of hands and whole hands from the two popular datasets exceeded the performance of the state-of-the-art approaches.

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