Multispectral Palmprint Recognition based on Fusion of Local Features

Amraoui, Amine; Fakhri, Yadolah; Kerroum, Mounir Ait

doi:10.1109/icmcs.2018.8525989

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…When using the CASIA database, the experimental results showed that we achieved the best accuracy result (98.55%) by fusing LBP and 2DLPP. Likewise, the scheme that was proposed in [4] was also evaluated by using a single spectral band from the CASIA database (with a wavelength of 940 nm). In this work (and in order to extract its discriminate feature), the compound local binary pattern (CLBP) was used.…”

Section: Literature Surveymentioning

confidence: 99%

Learning-Free Deep Features for Multispectral Palm-Print Classification

Bendjenna

Allah²,

Meraoumia³

2023

csci

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The feature extraction step is a major and crucial step in analyzing and understanding raw data as it has a considerable impact on the system accuracy. Unfortunately, despite the very acceptable results obtained by many handcrafted methods, they can have difficulty representing the features in the case of large databases or with strongly correlated samples. In this context, we proposed a new, simple and lightweight method for deep feature extraction. Our method can be configured to produce four different deep features, each controlled to tune the system accuracy. We have evaluated the performance of our method using a multispectral palmprint based biometric system and the experimental results, using the CASIA database, have shown that our method has high accuracy compared to many current handcrafted feature extraction methods and many well known deep learning based methods.

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Section: Literature Surveymentioning

confidence: 99%

Learning-Free Deep Features for Multispectral Palm-Print Classification

Bendjenna

Allah²,

Meraoumia³

2023

csci

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“…The 2D vector of contour points is traversed, and in doing so, the contours that form a u-shape are found. These arcs have a maximum distance of 1 8 of the convex hull's width and include a ± 45 orientation tolerance in case of extreme data acquisition cases.…”

Section: Shifting the Mic Effectivelymentioning

confidence: 99%

“…Palmprints are contained within a region of human hands and consist of ridges, similar to fingerprints, forming unique patterns. Palmprints may contain more useful information about humans than fingerprints as the area of the pattern is bigger [1,21,41]. However, this means that palmprint sensors are consequently bulkier and costlier than fingerprint sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Besides the similar ridge features of the fingerprint and palmprint, the palmprint also contains principal lines and wrinkles. This allows for capturing at lower resolutions and greater distances using cheaper sensors or DSLR cameras [1,30]. This motivated the construction of a robust palmprint recognition system in previous work [7], which included effective region of interest (ROI) detection for both constrained and 'unconstrained' palmprint image acquisition.…”

Section: Introductionmentioning

confidence: 99%

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Deep Palmprint Recognition with Alignment and Augmentation of Limited Training Samples

Brown

Bradshaw

2021

SN COMPUT. SCI.

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This paper builds upon a previously proposed automatic palmprint alignment and classification system. The proposed system was geared towards palmprints acquired from either contact or contactless sensors. It was robust to finger location and fist shape changes—accurately extracting the palmprints in images without fingers. An extension to this previous work includes comparisons of traditional and deep learning models, both with hyperparameter tuning. The proposed methods are compared with related verification systems and a detailed evaluation of open-set identification. The best results were yielded by a proposed Convolutional Neural Network, based on VGG-16, and outperforming tuned VGG-16 and Xception architectures. All deep learning algorithms are provided with augmented data, included in the tuning process, enabling significant accuracy gains. Highlights include near-zero and zero EER on IITD-Palmprint verification using one training sample and leave-one-out strategy, respectively. Therefore, the proposed palmprint system is practical as it is effective on data containing many and few training examples.

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“…Further, micro and macro-pattern histograms are extracted using local binary pattern (LBP) from different transformed bit planes and concatenated to form the feature vector. Several recent studies [13], [14] have suggested combining hierarchical multi-scale complete local binary pattern (HMS-CLBP) and weighted sparse representation-based classification (WSRC). However, basic LBP it is not rotation invariant, meaning that the LBP features will change if the image is rotated.…”

Section: Introductionmentioning

confidence: 99%

Palmprint recognition system using VR-LBP and KAZE features for better recognition accuracy

A. Khalid,

Imran Ahmad,

Shie Chow

et al. 2024

Bulletin EEI

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The palmprint recognition system has gained significant attention in security and law enforcement due to its unique features, such as principle lines, ridges, and wrinkles. However, many existing methods for extracting these features have limited accuracy, especially when the image illumination varies or the size of the processed pixels increases. Previous studies have shown that the local binary patterns (LBP) algorithm is effective for palmprint recognition due to the rich texture characteristics of a palmprint. In this paper, we propose a new technique for a robust contact-based palmprint identification system using vertical-LBP and KAZE feature detection. Our technique aims to improve recognition accuracy by using KAZE, which is a nonlinear diffusion approach that extracts nonlinear features from the evolution of the illuminance of an image. We also utilize principal component analysis (PCA) to reduce the dimensionality of the generated descriptor vector elements. The proposed method was tested on the PolyU database and achieved recognition accuracy of 99.7%.

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Multispectral Palmprint Recognition based on Fusion of Local Features

Cited by 7 publications

References 20 publications

Learning-Free Deep Features for Multispectral Palm-Print Classification

Learning-Free Deep Features for Multispectral Palm-Print Classification

Deep Palmprint Recognition with Alignment and Augmentation of Limited Training Samples

Palmprint recognition system using VR-LBP and KAZE features for better recognition accuracy

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