Cheyma nadir (52 risultati)

Condizione: New.

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

Condizione: New.

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

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

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New.

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

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

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

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

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.…

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

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 84 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -La biometría se está imponiendo como una de las soluciones más fiables para la identificación de personas en los sistemas de seguridad modernos. A diferencia de las contraseñas, se basa en características fisiológicas propias de cada persona, lo que ofrece un mayor nivel de seguridad.Esta obra se centra en dos modalidades prometedoras: las venas de los dedos y las huellas palmares. Aunque son estables y difíciles de falsificar, siguen siendo sensibles a las variaciones de iluminación y a la calidad de las imágenes capturadas. Se propone un enfoque que combina el aprendizaje profundo y el aprendizaje automático. Las características se extraen mediante redes convolucionales preentrenadas (VGG16, VGG19, MobileNetV2) y, a continuación, se refinan mediante extit{fine-tuning}. A continuación, se aplican clasificadores SVM, KNN y Random Forest, con una comparación entre los enfoques de instancia única y de múltiples instancias. MobileNetV2 ofrece el mejor rendimiento en precisión y eficiencia. El enfoque multiinstancia refuerza la robustez del sistema, mientras que el SVM destaca por su precisión en el reconocimiento. 68 pp. Spanisch.…

Paperback. Condizione: new. Paperback. La biometrie s'impose comme l'une des solutions les plus fiables pour l'identification des individus dans les systemes de securite modernes. Contrairement aux mots de passe, elle exploite des caracteristiques physiologiques propres a chaque personne, offrant un niveau de securite accru. Cet ouvrage porte sur deux modalites prometteuses: les veines des doigts et les empreintes palmaires. Stables et difficiles a falsifier, elles restent sensibles aux variations d'eclairage et a la qualite des images capturees. Une approche combinant apprentissage profond et apprentissage automatique est proposee. Les caracteristiques sont extraites via des reseaux convolutifs pre-entraines (VGG16, VGG19, MobileNetV2), puis affinees par \textit{fine-tuning}. Des classifieurs SVM, KNN et Random Forest sont ensuite appliques, avec une comparaison entre approches mono-instance et multi-instances. MobileNetV2 offre les meilleures performances en precision et en efficacite. L'approche multi-instances renforce la robustesse du systeme, tandis que le SVM se distingue par sa precision de reconnaissance. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condizione: 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.…

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.…

Taschenbuch. Condizione: 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.…

Taschenbuch. Condizione: 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. …

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.…

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. …

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 84 pp. Französisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - La biometría se está imponiendo como una de las soluciones más fiables para la identificación de personas en los sistemas de seguridad modernos. A diferencia de las contraseñas, se basa en características fisiológicas propias de cada persona, lo que ofrece un mayor nivel de seguridad.Esta obra se centra en dos modalidades prometedoras: las venas de los dedos y las huellas palmares. Aunque son estables y difíciles de falsificar, siguen siendo sensibles a las variaciones de iluminación y a la calidad de las imágenes capturadas. Se propone un enfoque que combina el aprendizaje profundo y el aprendizaje automático. Las características se extraen mediante redes convolucionales preentrenadas (VGG16, VGG19, MobileNetV2) y, a continuación, se refinan mediante extit{fine-tuning}. A continuación, se aplican clasificadores SVM, KNN y Random Forest, con una comparación entre los enfoques de instancia única y de múltiples instancias. MobileNetV2 ofrece el mejor rendimiento en precisión y eficiencia. El enfoque multiinstancia refuerza la robustez del sistema, mientras que el SVM destaca por su precisión en el reconocimiento.…

Paperback. Condizione: new. Paperback. A biometria afirma-se como uma das solucoes mais fiaveis para a identificacao de individuos nos sistemas de seguranca modernos. Ao contrario das palavras-passe, utiliza caracteristicas fisiologicas proprias de cada pessoa, oferecendo um nivel de seguranca mais elevado.Esta obra centra-se em duas modalidades promissoras: as veias dos dedos e as impressoes palmares. Estaveis e dificeis de falsificar, continuam, no entanto, sensiveis as variacoes de iluminacao e a qualidade das imagens captadas. E proposta uma abordagem que combina a aprendizagem profunda e a aprendizagem automatica. As caracteristicas sao extraidas atraves de redes convolucionais pre-treinadas (VGG16, VGG19, MobileNetV2) e, posteriormente, aperfeicoadas por \textit{fine-tuning}. Em seguida, sao aplicados classificadores SVM, KNN e Random Forest, com uma comparacao entre abordagens monoinstancia e multiinstancia. O MobileNetV2 oferece o melhor desempenho em termos de precisao e eficiencia. A abordagem multi-instancia reforca a robustez do sistema, enquanto o SVM se destaca pela sua precisao de reconhecimento. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Biometria staje sie jednym z najbardziej niezawodnych rozwiazan sluzacych do identyfikacji osob w nowoczesnych systemach bezpieczenstwa. W przeciwienstwie do hasel wykorzystuje ona cechy fizjologiczne charakterystyczne dla kazdej osoby, zapewniajac wyzszy poziom bezpieczenstwa.Niniejsza praca dotyczy dwoch obiecujacych metod: wzorow zyl palcow oraz odciskow dloni. Chociaz sa one stabilne i trudne do sfalszowania, pozostaja wrazliwe na zmiany oswietlenia oraz jakosc zarejestrowanych obrazow. Zaproponowano podejscie laczace uczenie glebokie z uczeniem maszynowym. Cechy sa wyodrebniane za pomoca wstepnie wytrenowanych sieci konwolucyjnych (VGG16, VGG19, MobileNetV2), a nastepnie udoskonalane poprzez \textit{fine-tuning}. Nastepnie stosuje sie klasyfikatory SVM, KNN i Random Forest, porownujac podejscia jednoinstancyjne i wieloinstancyjne. MobileNetV2 zapewnia najlepsza wydajnosc pod wzgledem dokladnosci i efektywnosci. Podejscie wieloinstancyjne zwieksza odpornosc systemu, natomiast SVM wyroznia sie precyzja rozpoznawania. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. La biometria si sta affermando come una delle soluzioni piu affidabili per l'identificazione delle persone nei moderni sistemi di sicurezza. A differenza delle password, si basa su caratteristiche fisiologiche specifiche di ogni individuo, garantendo un livello di sicurezza piu elevato.Questo lavoro si concentra su due modalita promettenti: le vene delle dita e le impronte palmari. Stabili e difficili da falsificare, rimangono tuttavia sensibili alle variazioni di illuminazione e alla qualita delle immagini acquisite. Viene proposto un approccio che combina il deep learning e l'apprendimento automatico. Le caratteristiche vengono estratte tramite reti convoluzionali pre-addestrate (VGG16, VGG19, MobileNetV2), per poi essere perfezionate tramite \textit{fine-tuning}. Vengono quindi applicati classificatori SVM, KNN e Random Forest, con un confronto tra approcci mono-istanza e multi-istanza. MobileNetV2 offre le migliori prestazioni in termini di precisione ed efficienza. L'approccio multi-istanza rafforza la robustezza del sistema, mentre l'SVM si distingue per la sua precisione di riconoscimento. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Die Biometrie hat sich als eine der zuverlaessigsten Loesungen zur Identifizierung von Personen in modernen Sicherheitssystemen etabliert. Im Gegensatz zu Passwoertern nutzt sie die fuer jeden Menschen einzigartigen physiologischen Merkmale und bietet somit ein hoeheres Mass an Sicherheit.Dieses Werk befasst sich mit zwei vielversprechenden Verfahren: den Fingervenen und den Handflaechenabdruecken. Diese sind zwar stabil und schwer zu faelschen, reagieren jedoch empfindlich auf Lichtveraenderungen und die Qualitaet der aufgenommenen Bilder. Es wird ein Ansatz vorgeschlagen, der Deep Learning und maschinelles Lernen kombiniert. Die Merkmale werden mithilfe vortrainierter konvolutioneller Netzwerke (VGG16, VGG19, MobileNetV2) extrahiert und anschliessend durch \textit{Fine-Tuning} verfeinert. Anschliessend werden SVM-, KNN- und Random-Forest-Klassifikatoren angewendet, wobei ein Vergleich zwischen Mono-Instanz- und Multi-Instanz-Ansaetzen erfolgt. MobileNetV2 bietet die beste Leistung hinsichtlich Genauigkeit und Effizienz. Der Multi-Instance-Ansatz erhoeht die Robustheit des Systems, waehrend sich der SVM durch seine Erkennungsgenauigkeit auszeichnet. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…