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

Perfeziona la tua ricerca

  • Libri (7)

  • Nuovo (7)

a

Fascia di prezzo personalizzata (EUR)

a

  • Lingua: Inglese

    Editore: Our Knowledge Publishing, 2026

    6630447387 / 9786630447385

    • Brossura

    Da: California Books, Miami, FL, U.S.A.California Books

    Venditore con 4 stelle
    Contatta il venditore

    Condizione: Nuovo

    EUR 59,38

     Spedizione gratuita 
    Spedito in U.S.A.

    Quantità: Più di 20 disponibili

    Condizione: New.

  • Lingua: Inglese

    Editore: Our Knowledge Publishing, 2026

    6630447387 / 9786630447385

    • Brossura

    Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK

    Venditore con 5 stelle
    Contatta il venditore

    Condizione: Nuovo

    EUR 57,42

    EUR 3,83 spedizione 
    Spedito da Regno Unito a U.S.A.

    Quantità: Più di 20 disponibili

    PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

  • Lingua: Inglese

    Editore: Our Knowledge Publishing, 2026

    6630447387 / 9786630447385

    • Brossura
    • Print on Demand

    Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail

    Venditore con 5 stelle
    Contatta il venditore

    Condizione: Nuovo

    EUR 59,69

     Spedizione gratuita 
    Spedito in U.S.A.

    Quantità: 1 disponibili

    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.

  • Lingua: Inglese

    Editore: Our Knowledge Publishing Aug 2026, 2026

    6630447387 / 9786630447385

    • Brossura
    • Print on Demand

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

    Venditore con 5 stelle
    Contatta il venditore

    Condizione: Nuovo

    EUR 48,90

    EUR 23,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 2 disponibili

    Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 60 pp. Englisch.

  • Lingua: Inglese

    Editore: Our Knowledge Publishing, 2026

    6630447387 / 9786630447385

    • Brossura
    • Print on Demand

    Da: CitiRetail, Stevenage, Regno UnitoCitiRetail

    Venditore con 5 stelle
    Contatta il venditore

    Condizione: Nuovo

    EUR 63,01

    EUR 43,13 spedizione 
    Spedito da Regno Unito a U.S.A.

    Quantità: 1 disponibili

    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.

  • Lingua: Inglese

    Editore: Our Knowledge Publishing, 2026

    6630447387 / 9786630447385

    • Brossura
    • Print on Demand

    Da: preigu, Osnabrück, Germaniapreigu

    Venditore con 5 stelle
    Contatta il venditore

    Condizione: Nuovo

    EUR 42,55

    EUR 70,00 spedizione 
    Spedito 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.

  • Lingua: Inglese

    Editore: Our Knowledge Publishing, 2026

    6630447387 / 9786630447385

    • Brossura
    • Print on Demand

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

    Venditore con 5 stelle
    Contatta il venditore

    Condizione: Nuovo

    EUR 99,64

    EUR 35,00 spedizione 
    Spedito 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.