Combination a Skeleton Filter and Reduction Dimension of Kernel PCA Based on Palmprint Recognition

Kusban, Muhammad; Susanto, Adhi; Wahyunggoro, Oyas

doi:10.11591/ijece.v6i6.11677

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…Supervised linear method PCA + LDA [42] Kernel method Kernel PCA and kernel Fisher discriminant applications [43,44] Tensor method 2DPCA [13] 2D LPP [25] Transform the domain Subspace methods in the transform domains [42] Invariant moments: Zernike moments [45] and Hu Invariant moments [46]…”

Section: The Approaches Work Representation Subspace Methodsmentioning

confidence: 99%

The Fundamentals of Unimodal Palmprint Authentication based on a Biometric System: A Review

Hussein¹,

Sahibuddin²,

Sjarif³

2018

ijacsa

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Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases.

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Section: The Approaches Work Representation Subspace Methodsmentioning

confidence: 99%

The Fundamentals of Unimodal Palmprint Authentication based on a Biometric System: A Review

Hussein¹,

Sahibuddin²,

Sjarif³

2018

ijacsa

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“…So, there is a large rotational variation, scaling and translation [34,40,41,42]. These images are taken in six spectral bands 700 nm ,460 nm, WHT(White Light), 630 nm, 850 nm and 940 nm [39,43]. This database is very difficult for an accurate extraction of ROI.…”

Section: ) (Roi) Extractionmentioning

confidence: 99%

“…The linear type has a difficult disadvantage of visually representing the object data in a graph and especially when using big data. In this work we used Kernel Principal Component Analysis (KPCA) as a reduction technique, which is a non-linear projection method and it is successfully used in biometrics [50,51,52]. This approach has proven its great capacity for reducing space and its good discriminating quality compared to the other approach.…”

Section: B Feature Extraction and Classificationmentioning

confidence: 99%

Improving Palmprint based Biometric System Performance using Novel Multispectral Image Fusion Scheme

Thamri¹,

Aloui²,

Naceur³

2020

IJACSA

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Nowadays, there are several identification systems which are based on different biometric modalities. In particular, multispectral images of palmprints captured in different spectral bands have a very distinctive biometric identifier. This paper proposes a novel fusion scheme of a biometric recognition system by multiSpectral palmprint. This system is composed of three blocks: (1) extraction of the region of interest (ROI) from multispectral images, (2) a new image fusion architecture based on the measurement of decorrelation, and (3) a scheme of dimension reduction and classification. The proposed image fusion system combines the information from the same left and right spectral band using the 2D discrete wavelet (DWT) transform technique. In addition, a feature extraction using the Log-Gabor transform is performed, while the feature size has been reduced using the Kernel Principal Component Analysis technique (KPCA). In Our experiments we use CASIA multispectral palmprint database. We obtained an accuracy rate (ACC) of 99.50% for the spectral bands WHT (white light) and 940 nm and an equal error rate EER = 0.05%.These results show that our system is robust against spoofing.

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“…This is usually carried out through the reduction or elimination of unwanted features  ISSN: 2088-8708 from the datasets [28][29][30]. Feature reduction in clinical data set is discussed in [31] and [32] explains the dimensionality reduction in kernel PCA.…”

Section: Introductionmentioning

confidence: 99%

Improved probabilistic distance based locality preserving projections method to reduce dimensionality in large datasets

Alostad¹

2019

IJECE

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<p>In this paper, a dimensionality reduction is achieved in large datasets using the proposed distance based Non-integer Matrix Factorization (NMF) technique, which is intended to solve the data dimensionality problem. Here, NMF and distance measurement aim to resolve the non-orthogonality problem due to increased dataset dimensionality. It initially partitions the datasets, organizes them into a defined geometric structure and it avoids capturing the dataset structure through a distance based similarity measurement. The proposed method is designed to fit the dynamic datasets and it includes the intrinsic structure using data geometry. Therefore, the complexity of data is further avoided using an Improved Distance based Locality Preserving Projection. The proposed method is evaluated against existing methods in terms of accuracy, average accuracy, mutual information and average mutual information.</p>

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Combination a Skeleton Filter and Reduction Dimension of Kernel PCA Based on Palmprint Recognition

Cited by 4 publications

References 13 publications

The Fundamentals of Unimodal Palmprint Authentication based on a Biometric System: A Review

The Fundamentals of Unimodal Palmprint Authentication based on a Biometric System: A Review

Improving Palmprint based Biometric System Performance using Novel Multispectral Image Fusion Scheme

Improved probabilistic distance based locality preserving projections method to reduce dimensionality in large datasets

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