Palmprint ROI Cropping Based on Enhanced Correlation Coefficient Maximisation Algorithm

Khalid, Noor Aldeen A.; Mandeel, Thulfiqar H.; Isa, Mohd Nazrin Md; Ahmad, Raja Abdullah Raja; Al-Dabagh, Mustafa Zuhaer Nayef

doi:10.1109/icset53708.2021.9612436

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…Equations In RoI extraction, it automatically and reliably clusters small regions of the captured palm that contain a lot of information when the user may pose naturally or deform in other ways, such as rotation, stretching, and scale variability [21,22,23,24]. Thus, RoI is an area localisation stage based on the classification and masking of a selected image part for processing.…”

Section: Methodsmentioning

confidence: 99%

Touchless palm print recognition system design using Gray Level Co-occurrence Matrix feature with K-Nearest Neighbor classification in MATLAB

Rochendi,

Silalahi,

Simanjuntak

2024

Sinergi

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This research designs a touchless fingerprint identification biometric system. This research problem stems from the user's need to change the conventional system to touchless in the hope of minimizing direct contact that can be scanned by someone who is not interested. This research aims to implement palm images in validating identity using GLCM (Gray-Level Co-occurrence Matrix) features with the K-Nearest Neighbours (K-NN) classification method in MATLAB. The identification process is divided into several stages: image acquisition, pre-processing, feature extraction with GLCM, and database matching using KNN classification. System testing uses the 10-fold cross validation method with 100 image samples (90 training images and 10 test images) that are tested in turn to calculate the average accuracy and analyze system performance. Furthermore, the test used GLCM angles (0o, 45o, 90o dan 135o) and K-NN with k values of 1, 5 and 7. The results showed the highest accuracy of 72% using an angle of 0° in GLCM and k=1 and the lowest at an angle of 90o and k=7 in K-NN. The advantage of this design is the recognition of the identity of the fingerprint owner in real time.

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Section: Methodsmentioning

confidence: 99%

Touchless palm print recognition system design using Gray Level Co-occurrence Matrix feature with K-Nearest Neighbor classification in MATLAB

Rochendi,

Silalahi,

Simanjuntak

2024

Sinergi

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“…This effect will have a significant impact on the performance of the recognition system to be built. The majority of existing methods use algorithms that perform an ROI extraction based on the limit of the palm, or key landmarks between the fingers, or some external factors [53,54].…”

Section: A Casia Multispectral Palmprint Databasementioning

confidence: 99%

Secure Palmprint Recognition based on Multispectral Sequential Capture

AMRAOUI¹,

KERROUM²,

Fakhri³

2022

IJACSA

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The security of personal identities is a serious challenge in today's digital world, with so many daily transactions requiring secure solutions. The use of biometric characteristics of the person is presented as the reliable solution to solve this problem. Indeed, this solution is effective, but it hides a weak point which lies in the ability to reproduce certain biometric characteristics for fraud. To overcome this weak point, we propose a secure approach for palmprints that relies on the concept of merging multiple features. Indeed, these features will be extracted from multi-spectral images with different spectra, which allow the extraction of information under the skin of the palm for two different spectrums sequentially in two different times (T1, T2) but instantly. The instant fusion of these characteristics will be impossible to replicate. The images used are grayscale. To satisfy a construction of a reliable and secure system, for this kind of patterns (palmprints), we will use the Compound Local Binary Pattern method, since this method adds an additional bit for each P bits coded by LBP corresponding to a neighbor of the local neighborhood, in order to build a robust system. This feature descriptor, it uses both the sign and tilt information of the differences between the central and neighboring gray values. The reliability of the proposed approach has been demonstrated on the Casia Multi-Spectral database. The final experimental results show reliable recognition rates and these recognition rates vary between 99% and 100% for the left and right palms.

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Palmprint ROI Cropping Based on Enhanced Correlation Coefficient Maximisation Algorithm

Cited by 2 publications

References 20 publications

Touchless palm print recognition system design using Gray Level Co-occurrence Matrix feature with K-Nearest Neighbor classification in MATLAB

Touchless palm print recognition system design using Gray Level Co-occurrence Matrix feature with K-Nearest Neighbor classification in MATLAB

Secure Palmprint Recognition based on Multispectral Sequential Capture

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