Isbn: 9786630119404 - deep learning-based palmprint recognition: transfer learning and fine-tuning approaches (4 risultati)

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  • Lingua: Inglese

    Editore: Globeedit Aug 2026, 2026

    6630119401 / 9786630119404

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    Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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    EUR 48,90

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    Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 84 pp. Englisch.

  • Lingua: Inglese

    Editore: Globeedit, 2026

    6630119401 / 9786630119404

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    Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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    EUR 70,86

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    Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This work presents a deep learning-based palmprint recognition system to address the growing need for reliable biometric authentication. Traditional security methods relying on passwords or tokens often fail to distinguish authorized users from fraudsters, motivating the shift toward biometrics.Three pre-trained CNN architectures are used: VGG16, MobileNetV2, and DenseNet-121, combined with two techniques: transfer learning and fine-tuning. Two data strategies are explored: single-instance using one hand, and multi-instance using both hands, evaluated on the PolyU palmprint database with 100 subjects.Fine-tuning consistently outperforms transfer learning. DenseNet-121 achieved the best accuracy of 98.75%, followed by MobileNetV2 at 98.50% and VGG16 at 92.50%. Transfer learning yielded lower but competitive results. The multi-instance strategy produced stronger performance by providing richer biometric information from both hands.

  • Lingua: Inglese

    Editore: Globeedit Aug 2026, 2026

    6630119401 / 9786630119404

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    Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

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    EUR 48,90

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    Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This work presents a deep learning-based palmprint recognition system to address the growing need for reliable biometric authentication. Traditional security methods relying on passwords or tokens often fail to distinguish authorized users from fraudsters, motivating the shift toward biometrics.Three pre-trained CNN architectures are used: VGG16, MobileNetV2, and DenseNet-121, combined with two techniques: transfer learning and fine-tuning. Two data strategies are explored: single-instance using one hand, and multi-instance using both hands, evaluated on the PolyU palmprint database with 100 subjects.Fine-tuning consistently outperforms transfer learning. DenseNet-121 achieved the best accuracy of 98.75%, followed by MobileNetV2 at 98.50% and VGG16 at 92.50%. Transfer learning yielded lower but competitive results. The multi-instance strategy produced stronger performance by providing richer biometric information from both hands. 84 pp. Englisch.

  • Lingua: Inglese

    Editore: GlobeEdit, 2026

    6630119401 / 9786630119404

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    Da: preigu, Osnabrück, Germaniapreigu

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    EUR 42,55

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    Quantità: 5 disponibili

    Taschenbuch. Condizione: Neu. Deep Learning-Based Palmprint Recognition | Transfer Learning and Fine-Tuning Approaches | Cheyma Nadir | Taschenbuch | Englisch | 2026 | GlobeEdit | EAN 9786630119404 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.