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Deep-Learning-Ansatz für Erkennungs-systeme
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ISBN10: 6630444825
ISBN13: 9786630444827
Publisher: Verlag Unser Wissen
Pages: 68
Weight: 0.23
Height: 0.16 Width: 6.00 Depth: 9.00
Language: German
ISBN13: 9786630444827
Publisher: Verlag Unser Wissen
Pages: 68
Weight: 0.23
Height: 0.16 Width: 6.00 Depth: 9.00
Language: German
Die Biometrie hat sich als eine der zuverlässigsten Lösungen zur Identifizierung von Personen in modernen Sicherheitssystemen etabliert. Im Gegensatz zu Passwörtern nutzt sie die für jeden Menschen einzigartigen physiologischen Merkmale und bietet somit ein höheres Ma an Sicherheit.Dieses Werk befasst sich mit zwei vielversprechenden Verfahren: den Fingervenen und den Handflächenabdrücken. Diese sind zwar stabil und schwer zu fälschen, reagieren jedoch empfindlich auf Lichtveränderungen und die Qualität 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 anschlie end durch \textit{Fine-Tuning} verfeinert. Anschlie end werden SVM-, KNN- und Random-Forest-Klassifikatoren angewendet, wobei ein Vergleich zwischen Mono-Instanz- und Multi-Instanz-Ansätzen erfolgt. MobileNetV2 bietet die beste Leistung hinsichtlich Genauigkeit und Effizienz. Der Multi-Instance-Ansatz erhöht die Robustheit des Systems, während sich der SVM durch seine Erkennungsgenauigkeit auszeichnet.
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