Finger knuckle pattern person authentication system based on monogenic and LPQ features

Lakshmanan, Sathiya; Velliyan, Palanisamy; Attia, Abdelouahab; Chalabi, Nour Elhouda

doi:10.1007/s10044-021-01047-y

Cited by 4 publications

(2 citation statements)

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“…Automated fruit decay detection systems based on computer vision and machine learning techniques have demonstrated promising results in terms of accuracy, speed, and cost-effectiveness ( Jayasena et al., 2015 ; Boulent et al., 2019 ; Lakshmanan, 2019 ). These systems contribute to reducing post-harvest losses, optimizing storage conditions, and enhancing the overall efficiency of the fruit supply chain.…”

Section: Introductionmentioning

confidence: 99%

Robust deep learning method for fruit decay detection and plant identification: enhancing food security and quality control

Afsharpour,

Zoughi,

Deypir

et al. 2024

Front. Plant Sci.

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This paper presents a robust deep learning method for fruit decay detection and plant identification. By addressing the limitations of previous studies that primarily focused on model accuracy, our approach aims to provide a more comprehensive solution that considers the challenges of robustness and limited data scenarios. The proposed method achieves exceptional accuracy of 99.93%, surpassing established models. In addition to its exceptional accuracy, the proposed method highlights the significance of robustness and adaptability in limited data scenarios. The proposed model exhibits strong performance even under the challenging conditions, such as intense lighting variations and partial image obstructions. Extensive evaluations demonstrate its robust performance, generalization ability, and minimal misclassifications. The inclusion of Class Activation Maps enhances the model’s capability to identify distinguishing features between fresh and rotten fruits. This research has significant implications for fruit quality control, economic loss reduction, and applications in agriculture, transportation, and scientific research. The proposed method serves as a valuable resource for fruit and plant-related industries. It offers precise adaptation to specific data, customization of the network architecture, and effective training even with limited data. Overall, this research contributes to fruit quality control, economic loss reduction, and waste minimization.

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Section: Introductionmentioning

confidence: 99%

Robust deep learning method for fruit decay detection and plant identification: enhancing food security and quality control

Afsharpour,

Zoughi,

Deypir

et al. 2024

Front. Plant Sci.

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“…Also, palmprint contains huge texture information, which provides a meaningful datafor person recognition [14] . While, FKP also has distinctive anatomical structures.FKP has been recently studied to ameliorate biometric authentication system with higher accuracy [12,29] . It is well-known fact that any unimodal hand biometric systems are not totally ideal to be used in absolute security applications.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-driven palmprint and finger knuckle pattern-based multimodal Person recognition system

Attia

Maza

Akhtar

et al. 2022

Multimed Tools Appl

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Fusion of deep and local gradient-based features for multimodal finger knuckle print identification

Aiadi,

Khaldi,

Korichi

et al. 2024

Cluster Comput

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Finger knuckle pattern person authentication system based on monogenic and LPQ features

Cited by 4 publications

References 50 publications

Robust deep learning method for fruit decay detection and plant identification: enhancing food security and quality control

Robust deep learning method for fruit decay detection and plant identification: enhancing food security and quality control

Deep learning-driven palmprint and finger knuckle pattern-based multimodal Person recognition system

Fusion of deep and local gradient-based features for multimodal finger knuckle print identification

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