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.
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Paperback. Condición: 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. Nº de ref. del artículo: 9786630447385
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Paperback. Condición: 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. Nº de ref. del artículo: 9786630447385
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 136372655
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9786630447385
Cantidad disponible: 2 disponibles