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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.
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Über die Autorin bzw. den Autor
Cheyma NADIR was born on August 20, 1996, in M'Sila, Algeria. She received her Master's degree in embedded systems electronics from Mohamed Boudiaf University of M'Sila in 2020 and earned her Ph.D. in the same field from the same university in 2025. Her research interests include biometric systems, artificial intelligence, and image processing.
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Deep Learning-Based Palmprint Recognition
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Deep Learning-Based Palmprint Recognition : Transfer Learning and Fine-Tuning Approaches
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786630119404
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Deep Learning-Based Palmprint Recognition
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786630119404
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Deep Learning-Based Palmprint Recognition | Transfer Learning and Fine-Tuning Approaches
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 136513856
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