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Approccio di apprendimento profondo per i sistemi di riconoscimento
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ISBN10: 6630350111
ISBN13: 9786630350111
Publisher: Edizioni Sapienza
Pages: 68
Weight: 0.23
Height: 0.16 Width: 6.00 Depth: 9.00
Language: Italian
ISBN13: 9786630350111
Publisher: Edizioni Sapienza
Pages: 68
Weight: 0.23
Height: 0.16 Width: 6.00 Depth: 9.00
Language: Italian
La biometria si sta affermando come una delle soluzioni più 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 più elevato.Questo lavoro si concentra su due modalità promettenti: le vene delle dita e le impronte palmari. Stabili e difficili da falsificare, rimangono tuttavia sensibili alle variazioni di illuminazione e alla qualità 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.
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