Biometric Recognition of Finger Knuckle Print Based on the Fusion of Global Features and Local Features

Li, Wenwen

doi:10.1155/2022/6041828

Cited by 6 publications

(8 citation statements)

References 23 publications

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“…However, finger knuckle images need its own unique coordinate system. These coordinates can be used to trim an area of interest (ROI) from the source image in preparation for feature extraction 24 . To improve personal identification methods based on “major–minor” finger knuckle shapes, particular ROI image extraction is required.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Human recognition system through major and minor finger knuckle pattern fusion by symmetric-sum method

Babalola,

Hamidu,

Toygar

2023

J. Electron. Imag.

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.Biometric feature-based personal identification is gaining popularity these days since it is more trustworthy than previous approaches and has a wide range of applications. Furthermore, hand-based biometrics have better user acceptability, making finger knuckles a relatively acceptable trait due to their location and the added benefit of being less susceptible to harm. Finger knuckle prints are the inherent skin patterns that form at the knuckles of the back of the hand and have been proven to be extremely rich in textures, hence they may be utilized to uniquely identify a person. Our study seeks to explore the innate advantage of finger knuckles and the benefit of combining multiple features from knuckle areas, minor and major, using symmetric sum algorithms to fuse scores from each of the traits. The proposed methodology is tested on a modified AlexNet model as well as the original AlexNet, modified ResNet50, binarized statistical image features, and principal component analysis for comparison. Experimental results are obtained using PolyUKnuckleV1 finger knuckle datasets provided by Hong Kong Polytechnic University. The results reinforced the idea that multimodal biometric systems are stronger and that finger knuckles are unique to each person.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…Two-layer serial fusion method has also been recommended using a technique that combines both global and local properties. The technique uses PCA for global feature extraction and LBP for local feature 10 …”

Section: Literature Reviewmentioning

confidence: 99%

Human recognition system through major and minor finger knuckle pattern fusion by symmetric-sum method

Babalola,

Hamidu,

Toygar

2023

J. Electron. Imag.

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“…Then, a blend of backtracking search estimation and 2D2LDA has been used to pick the overarching palm and knuckle features for grouping. A technique consolidating worldwide elements and neighborhood highlights for the acknowledgment of FKP pictures presented by [15].…”

Section: -Related Workmentioning

confidence: 99%

Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods

Al-juboori

Abed

2022

Bulletin EEI

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Biometric-based individual distinguishing proof is a successful strategy for consequently perceiving, with high certainty, an individual's character. The utilization of finger knuckle pictures for individual ID has shown promising outcomes and produced a ton of interest in biometrics. By seeing that the surface example delivered by twisting the finger knuckle is profoundly particular, in this paper we present a new biometric validation framework utilizing finger-knuckle-print (FKP) imaging. In this paper, another methodology in view of neighborhood surface examples is proposed. Local derivative pattern (LDP) histogram is investigated for FKP description. Then based on neighborhood preserving embedding (NPE) is used for dimension reduction to the feature vector. The feature extraction method is computed and evaluated in the identification framework task. The machine learning methods (multiclass support vector machine (MSVM), random forest (RF), k-nearest neighbor (KNN)) are proposed for classification. The system is tested on the PolyU finger knuckle database. The empirical results proved that the proposed model has the best results with RF. Moreover, our proposed LDP-NPE model has been evaluated and the results show remarkable efficiency in comparison with previous work. Experimentally, the proposed model has better accuracy as reflected by 99.65%.

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“…An example image is shown in Figure . 1. The texture is a unique feature and is widely used in image recognition, such as biology [1], medicine [2], and materials [3]. The texture of the same things often has the same characteristics.…”

Section: Introductionmentioning

confidence: 99%

Skin texture recognition of leg images based on chaotic mapping reorganization feature

Tang¹,

Huang²

2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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In criminal and victim identification, when information such as fingerprint and facial images cannot be obtained, the development of other new bio-metric recognition has become an important task. It is found from investigations that the lower leg is often exposed in an evidence image. If we can use the information to identify the identity of the suspect, it will be a great break-through for criminal investigation. Unlike faces or fingerprints, the image of a lower leg does not have many discriminative features. Therefore, capturing more information from an image has become the key to the success of recognition. Despite the rapid development of deep learning in recent years and the wide application of convolutional neural networks in biometric recognition, they are very dependent on a large amount of training data. In this paper, we propose a recognition method based on the combination of chaotic mapping and local binary pattern (LBP), which captures the nonlinear features of the image. Experimental results show that the method proposed in this paper has a better recognition rate and an encouraging performance.

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Biometric Recognition of Finger Knuckle Print Based on the Fusion of Global Features and Local Features

Cited by 6 publications

References 23 publications

Human recognition system through major and minor finger knuckle pattern fusion by symmetric-sum method

Human recognition system through major and minor finger knuckle pattern fusion by symmetric-sum method

Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods

Skin texture recognition of leg images based on chaotic mapping reorganization feature

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