Paperback. Etat : 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. N� de r�f. du vendeur 9786630447385

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A Deep Learning Approach for Recognition Systems

Nadir, Cheyma

Edit� par Our Knowledge Publishing, 2026

ISBN 10 : 6630447387 ISBN 13 : 9786630447385

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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N� de r�f. du vendeur L2-9786630447385

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A Deep Learning Approach for Recognition Systems

Cheyma Nadir

Edit� par Our Knowledge Publishing Aug 2026, 2026

ISBN 10 : 6630447387 ISBN 13 : 9786630447385

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 60 pp. Englisch. N� de r�f. du vendeur 9786630447385

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A Deep Learning Approach for Recognition Systems (Paperback)

Cheyma Nadir

Edit� par Our Knowledge Publishing, 2026

ISBN 10 : 6630447387 ISBN 13 : 9786630447385

Neuf Paperback

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Paperback. Etat : 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. N� de r�f. du vendeur 9786630447385

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A Deep Learning Approach for Recognition Systems

Cheyma Nadir

Edit� par Our Knowledge Publishing, 2026

ISBN 10 : 6630447387 ISBN 13 : 9786630447385

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A Deep Learning Approach for Recognition Systems

Cheyma Nadir

Edit� par Our Knowledge Publishing, 2026

ISBN 10 : 6630447387 ISBN 13 : 9786630447385

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9786630447385

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