Finger-Knuckle-Print Recognition Using Deep Convolutional Neural Network

Trabelsi, Selma; Samai, Djamel; Meraoumia, Abdallah; Bensid, Khaled; Benlamoudi, Azeddine; Dornaika, Fadi; Taleb‐Ahmed, Abdelmalik

doi:10.1109/ccssp49278.2020.9151531

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…The proposed system is also compared with the state-of-the-art, including those from Ref. 7, 11, and 25–27, as shown in Table 5. Both unimodal and multimodal systems are compared in terms of their recognition results in this table.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Generally, it can be seen that the suggested approach performs better than the research in Refs. 7, 11, and 25–27, with the exception of Ref. 26, which has a 100% recognition rate.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…7, 11, and 25–27, with the exception of Ref. 26, which has a 100% recognition rate. The comparison of the data in Table 5 demonstrates that the proposed methodology performs favorably against state-of-the-art methods in finger knuckle biometrics techniques.…”

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%

“…Generally, it can be seen that the suggested approach performs better than the research in Refs. 7, 11, and 25–27, with the exception of Ref. 26, which has a 100% recognition rate.…”

Section: Experiments and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…Automated identification solutions have become critical for security and privacy in today's digitally linked world [1]. Biometrics is a type of person recognition method; it is a security solution that differs from typical authentication and identification techniques such as passwords, ID cards, and PIN codes [2].…”

Section: Introductionmentioning

confidence: 99%

“…It is a hand-based characteristic in which images include information about an individual's finger knuckle lines and textures, which may become a unique anatomical feature and be used to identify a person. These characteristics were proposed and examined in order to overcome some of the limits and shortcomings of earlier hand-based technologies [65], as well as the inconsistencies of the low-cost [2] and small-size imaging equipment used to capture the structure [66]. Local feature extraction is performed on the FKP system, followed by a mixture of local and global feature extractions, geometric feature extraction, and ultimately an enhanced acquisition device that includes both major and minor intrinsic features [11,[67][68][69][70].…”

Section: Introductionmentioning

confidence: 99%

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

et al. 2022

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Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system.

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A comprehensive survey and deep learning-based approach for human recognition using ear biometric

2021

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Human recognition systems based on biometrics are much in demand due to increasing concerns of security and privacy. The human ear is unique and useful for recognition. It offers numerous advantages over popular biometrics traits face, iris, and fingerprints. A lot of work has been attributed to ear biometric, and the existing methods have achieved remarkable success over constrained databases. However, in unconstrained environment, a significant level of difficulty is observed as the images experience various challenges. In this paper, we first have provided a comprehensive survey on ear biometric using a novel taxonomy. The survey includes in-depth details of databases, performance evaluation parameters, and existing approaches. We have introduced a new database, NITJEW, for evaluation of unconstrained ear detection and recognition. A modified deep learning models Faster-RCNN and VGG-19 are used for ear detection and ear recognition tasks, respectively. The benchmark comparative assessment of our database is performed with six existing popular databases. Lastly, we have provided insight into open-ended research problems worth examining in the near future. We hope that our work will be a stepping stone for new researchers in ear biometrics and helpful for further development.

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Finger-Knuckle-Print Recognition Using Deep Convolutional Neural Network

Cited by 9 publications

References 14 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

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

A comprehensive survey and deep learning-based approach for human recognition using ear biometric

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