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Deep Learning-Based Palmprint Recognition
Impresión bajo demandaLibrería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 84 pp. Englisch. Nº de ref. del artículo: 9786630119404
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Deep Learning-Based Palmprint Recognition : Transfer Learning and Fine-Tuning Approaches
Impresión bajo demandaLibrería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9786630119404
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Deep Learning-Based Palmprint Recognition
Impresión bajo demandaLibrería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9786630119404
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EUR 48,90
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Deep Learning-Based Palmprint Recognition | Transfer Learning and Fine-Tuning Approaches
Impresión bajo demandaLibrería: preigu, Osnabrück, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 136513856
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EUR 42,80
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Se envía de Alemania a Estados Unidos de America
Se envía de Alemania a Estados Unidos de America
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