Isbn: 9786630447385 - a deep learning approach for recognition systems (7 risultati)

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Da: California Books, Miami, FL, U.S.A.California Books
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EUR 59,38
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Condizione: New.

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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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EUR 57,42
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condizione: new. Paperback. Biometrics is emerging as one of the most reliable solutions for identifying individuals in modern security systems. Unlike passwords, it relies on physiological characteristics unique to each person, offering a higher level of security.This book focuses on two promising modalities: finger veins and palm prints. While stable and difficult to forge, they remain sensitive to variations in lighting and the quality of the captured images. An approach combining deep learning and machine learning is proposed. Features are extracted using pre-trained convolutional neural networks (VGG16, VGG19, MobileNetV2) and then refined through \textit{fine-tuning}. SVM, KNN, and Random Forest classifiers are then applied, with a comparison between single-instance and multi-instance approaches. MobileNetV2 offers the best performance in terms of accuracy and efficiency. The multi-instance approach enhances the system's robustness, while SVM stands out for its recognition accuracy. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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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 60 pp. Englisch.

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Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
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EUR 63,01
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Paperback. Condizione: new. Paperback. Biometrics is emerging as one of the most reliable solutions for identifying individuals in modern security systems. Unlike passwords, it relies on physiological characteristics unique to each person, offering a higher level of security.This book focuses on two promising modalities: finger veins and palm prints. While stable and difficult to forge, they remain sensitive to variations in lighting and the quality of the captured images. An approach combining deep learning and machine learning is proposed. Features are extracted using pre-trained convolutional neural networks (VGG16, VGG19, MobileNetV2) and then refined through \textit{fine-tuning}. SVM, KNN, and Random Forest classifiers are then applied, with a comparison between single-instance and multi-instance approaches. MobileNetV2 offers the best performance in terms of accuracy and efficiency. The multi-instance approach enhances the system's robustness, while SVM stands out for its recognition accuracy. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

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Da: preigu, Osnabrück, Germaniapreigu
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EUR 42,55
EUR 70,00 spedizioneSpedito da Germania a U.S.A.Quantità: 5 disponibili
Taschenbuch. Condizione: Neu. A Deep Learning Approach for Recognition Systems | Cheyma Nadir | Taschenbuch | Englisch | 2026 | Our Knowledge Publishing | EAN 9786630447385 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…

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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 99,64
EUR 35,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Biometrics is emerging as one of the most reliable solutions for identifying individuals in modern security systems. Unlike passwords, it relies on physiological characteristics unique to each person, offering a higher level of security.This book focuses on two promising modalities: finger veins and palm prints. While stable and difficult to forge, they remain sensitive to variations in lighting and the quality of the captured images. An approach combining deep learning and machine learning is proposed. Features are extracted using pre-trained convolutional neural networks (VGG16, VGG19, MobileNetV2) and then refined through extit{fine-tuning}. SVM, KNN, and Random Forest classifiers are then applied, with a comparison between single-instance and multi-instance approaches. MobileNetV2 offers the best performance in terms of accuracy and efficiency. The multi-instance approach enhances the system's robustness, while SVM stands out for its recognition accuracy. …