2D log-Gabor filters for competitive coding-based multi-spectral palmprint recognition

Bounneche, Meriem D.; Boubchir, Larbi; Ali-Chérif, Arab; Bouridane, Ahmed; Nekhoul, Bachir

doi:10.1109/tsp.2016.7760969

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…This was demonstrated through experiments where clear improvements over RLOC and the competitive coding technique were noticed. Bounneche et al [10] claimed the suitability of the 2D log-Gabor filter for multispectral palmprint identification where competitive coding was adopted. In [11], the Gabor filter was also adopted with six different orientations.…”

Section: Introductionmentioning

confidence: 99%

Self‐geometric relationship filter for efficient SIFT key‐points matching in full and partial palmprint recognition

Almaghtuf

Khelifi

2018

IET biom.

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Recently, palmprints have been broadly reported in the literature as an effective biometric modality. Although scaleinvariant feature transform (SIFT)-based features have been proven to be robust against image transformations and deformations, SIFT has not been as successful as other methods in palmprint recognition. In fact, SIFT-based identification has been widely criticised in biometrics due to its high false matching rate. To overcome this weakness, a new filtering method for SIFT-based palmprint matching, called the self-geometric relationship-based filter (SGR-filter) is presented. While existing SIFT matching considers only the relationship between the SIFT points of the query image, on one hand, and their corresponding points in the reference image, on the other hand, SGR-filtering further takes into account the geometric relationship between SIFT points within the query image in comparison with the relationship of the corresponding matched points in the reference image. Assessed with the proposed SGR-filter on various datasets, the SIFT-based palmprint recognition system has been shown to deliver significantly higher performance when compared with the conventional SIFT matching as well as another related key-points filtering technique. Furthermore, experimental results on a number of different full and partial palmprint datasets have shown the superiority of the proposed system over state-of-the-art techniques.

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Section: Introductionmentioning

confidence: 99%

Self‐geometric relationship filter for efficient SIFT key‐points matching in full and partial palmprint recognition

Almaghtuf

Khelifi

2018

IET biom.

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Competitive Coding Scheme based on 2D Log-Gabor filter for Palm Vein Recognition

Boubchir

Aberni

Daachi

2018

2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)

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Multispectral Palmprint Recognition: A Survey and Comparative Study

Aberni

Boubchir

Daachi

2019

J CIRCUIT SYST COMP

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Multispectral palmprint recognition has been investigated for many problems and applications over the last decade. It has become one of the most well-known biometric recognition systems. Its success is due to the rich features that can be extracted and exploited from the multispectral images of palmprint captured within specific wavelength ranges across the electromagnetic spectrum. This paper provides an overview of recent state-of-the-art multispectral palmprint approaches for person recognition. The approaches surveyed are discussed by describing, in particular, their feature extraction, feature fusion, matching and decision algorithms. Finally, a comparative study to evaluate their performances for both verification and identification modes is addressed.

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2D log-Gabor filters for competitive coding-based multi-spectral palmprint recognition

Cited by 3 publications

References 11 publications

Self‐geometric relationship filter for efficient SIFT key‐points matching in full and partial palmprint recognition

Self‐geometric relationship filter for efficient SIFT key‐points matching in full and partial palmprint recognition

Competitive Coding Scheme based on 2D Log-Gabor filter for Palm Vein Recognition

Multispectral Palmprint Recognition: A Survey and Comparative Study

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