A Robust Technique to Characterize the Palmprint using Radon transform and Delaunay triangulation

Bouchemha, Amel; Naït‐Ali, Amine; Doghmane, Nourredine

doi:10.5120/1515-1895

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…The mainstream algorithms is mainly in three ways: triangulation growth algorithm, incremental insertion algorithm and divide-and-conquer algorithm. The application of the last two algorithms are more popular, combination of two algorithms are also often used in data processing [6][7][8].…”

Section: Overview Of Delaunay Triangulation Algorithmmentioning

confidence: 99%

The Design of Weldment Model Based on Improved Delaunay Triangulation Algorithm

Sun

Wang

Huang

2013

AMM

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By using Delaunay triangulation algorithm, triangulating with split-merge algorithm and the incremental insertion algorithm, improving the judgment that if the new inserted point meets the Delaunay triangulation "empty circle" criterion in synthesis algorithm, meanwhile, with the corresponding improvement of the LOP optimization algorithm, the design of weldment model in the virtual welding system has achieved good result.

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Section: Overview Of Delaunay Triangulation Algorithmmentioning

confidence: 99%

The Design of Weldment Model Based on Improved Delaunay Triangulation Algorithm

Sun

Wang

Huang

2013

AMM

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“…Other biometric techniques are currently being developed such as biometry by the geometry of the veins of the hand [21], biometry by the palm print [8], biometry by the geometry of the veins of the finger [25]. Despite their reliability, biometric identification systems do not guarantee recognition of the individual.…”

Section: Introductionmentioning

confidence: 99%

Revolutionary AI-Driven Skeletal Fingerprinting for Remote Individual Identification

BERRAJAA,

OUTMANI,

BERRAJAA

et al. 2024

ijacsa

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This research aims to devise a distinct mathematical key for individual identification and recognition. This key, represented through signals, is constructed using Lagrange polynomials derived from the skeletal points. Consequently, we present this key as a novel fingerprint categorized within physiological fingerprints. It's crucial to highlight that the primary application of this fingerprint is for remote individual identification, specifically excluding any bodily masking. Subsequently, we implement an artificial intelligence model, specifically a Convolutional Neural Network (CNN), for the automated detection of individuals. The proposed CNN is trained on an extensive dataset comprising 10000 real-world cases and augmented data. Our skeletal fingerprint recognition system demonstrates superior performance compared to other physiological fingerprints, achieving a remarkable 98% accuracy in detecting individuals at a distance.

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“…Methods that extract a region of fixed size does not take full advantage of the large area offered by the palm. The ROI extraction of the region, in general, is based on key points [1, 4,16,25]. Other works [7,14] proposed to use the hand curves for the ROI extraction.…”

Section: Introductionmentioning

confidence: 99%

Extraction method of Region of Interest from hand palm: Application with contactless and touchable devices

Artabaz

Benatchba

Koudil

et al. 2014

2014 10th International Conference on Information Assurance and Security

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Palm print is one of the modalities that offer high recognition accuracy. The recognition process depends on an optimized ROI (Region of Interest) extraction. This extraction is affected by several factors including the device used and the acquisition conditions. The acquisition mode can alter some image properties like rotation, translation and scale. Some devices are designed to maintain hand in a fixed position and delimit a subspace of the hand. On the other hand, contactlessdevices offer more convenience and flexibility but lead to altered images. ROI extraction methods must consider the acquisition device (with contact or contactless). In this paper, we propose a ROI extraction method that addresses this issue.We test our method on two databases PolyU and CASIA which illustrate the impact of using contactless device unlike the PolyU device. Then, we test performances of the palmprint biometric system. We use a Fisher Linear Discriminant projection (FLD) to extract features from ROI transformed into the frequency domain. Our proposed method can significantly cover a great portion of the palm in the two databases.Performances obtained with the proposed palmprint system are promising.

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A Robust Technique to Characterize the Palmprint using Radon transform and Delaunay triangulation

Cited by 9 publications

References 25 publications

The Design of Weldment Model Based on Improved Delaunay Triangulation Algorithm

The Design of Weldment Model Based on Improved Delaunay Triangulation Algorithm

Revolutionary AI-Driven Skeletal Fingerprinting for Remote Individual Identification

Extraction method of Region of Interest from hand palm: Application with contactless and touchable devices

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