Multispectral palmprint recognition using Pascal coefficients-based LBP and PHOG descriptors with random sampling

El-Tarhouni, Wafa; Boubchir, Larbi; Elbendak, Mosa; Bouridane, Ahmed

doi:10.1007/s00521-017-3092-7

Cited by 16 publications

(11 citation statements)

References 24 publications

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“…The palmprint images contain rich features such as principal lines, wrinkles, and minutiae. They are relatively stable, and their captured images are easy to obtain [8,9]. They can be categorized according to the way of their acquisition.…”

Section: Introductionmentioning

confidence: 99%

Efficient palmprint biometric identification systems using deep learning and feature selection methods

Trabelsi

Samai

Dornaika

et al. 2022

Neural Comput & Applic

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Over the past two decades, several studies have paid great attention to biometric palmprint recognition. Recently, most methods in literature adopted deep learning due to their high recognition accuracy and the capability to adapt with different acquisition palmprint images. However, high-dimensional data with a large number of uncorrelated and redundant features remain a challenge due to computational complexity issues. Feature selection is a process of selecting a subset of relevant features, which aims to decrease the dimensionality, reduce the running time, and improve the accuracy. In this paper, we propose efficient unimodal and multimodal biometric systems based on deep learning and feature selection. Our approach called simplified PalmNet–Gabor concentrates on the improvement of the PalmNet for fast recognition of multispectral and contactless palmprint images. Therefore, we used Log-Gabor filters in the preprocessing to increase the contrast of palmprint features. Then, we reduced the number of features using feature selection and dimensionality reduction procedures. For the multimodal system, we fused modalities at the matching score level to improve system performance. The proposed method effectively improves the accuracy of the PalmNet and reduces the number of features as well the computational time. We validated the proposed method on four public palmprint databases, two multispectral databases, CASIA and PolyU, and two contactless databases, Tongji and PolyU 2D/3D. Experiments show that our approach achieves a high recognition rate while using a substantially lower number of features.

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Section: Introductionmentioning

confidence: 99%

Efficient palmprint biometric identification systems using deep learning and feature selection methods

Trabelsi

Samai

Dornaika

et al. 2022

Neural Comput & Applic

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“…Another category that is also used, this category relies on statistics-based approaches; they have shown good performance [28][29]. A multitude of statistical methods have been employed in this category such as variance, standard deviation, energy and histograms of local binary models [30], Zernike and Hu moments [31]. Some transformations have also been used such as the wavelet transform to convert the palmprint image into a small number of wavelet coefficients, and then calculate the variance and the mean of these coefficients to generate the image characteristics [32].…”

Section: Introductionmentioning

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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“…Their study might not be comprehensive despite the good results since it has not addressed rotation, displacement, or image scaling issues. El‐Tarhouni, Boubchir, Elbendak, and Bouridane (2019) presented an approach based on pyramid histogram orientation gradient and Pascal coefficients of local binary patterns to improve the identity verification rate using palm print images. Their research is composed of a few stages.…”

Section: Introductionmentioning

confidence: 99%

Identity verification using palm print microscopic images based on median robust extended local binary pattern features and k‐nearest neighbor classifier

Rehman

Harouni

Karchegani

et al. 2021

Microscopy Res & Technique

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Automatic identity verification is one of the most critical and research‐demanding areas. One of the most effective and reliable identity verification methods is using unique human biological characteristics and biometrics. Among all types of biometrics, palm print is recognized as one of the most accurate and reliable identity verification methods. However, this biometrics domain also has several critical challenges: image rotation, image displacement, change in image scaling, presence of noise in the image due to devices, region of interest (ROI) detection, or user error. For this purpose, a new method of identity verification based on median robust extended local binary pattern (MRELBP) is introduced in this study. In this system, after normalizing the images and extracting the ROI from the microscopic input image, the images enter the feature extraction step with the MRELBP algorithm. Next, these features are reduced by the dimensionality reduction step, and finally, feature vectors are classified using the k‐nearest neighbor classifier. The microscopic images used in this study were selected from IITD and CASIA data sets, and the identity verification rate for these two data sets without challenge was 97.2% and 96.6%, respectively. In addition, computed detection rates have been broadly stable against changes such as salt‐and‐pepper noise up to 0.16, rotation up to 5°, displacement up to 6 pixels, and scale change up to 94%.

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Multispectral palmprint recognition using Pascal coefficients-based LBP and PHOG descriptors with random sampling

Cited by 16 publications

References 24 publications

Efficient palmprint biometric identification systems using deep learning and feature selection methods

Efficient palmprint biometric identification systems using deep learning and feature selection methods

Secure Palmprint Recognition based on Multispectral Sequential Capture

Identity verification using palm print microscopic images based on median robust extended local binary pattern features and k‐nearest neighbor classifier

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