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IMPLEMENTASI MODEL EUM PADA FACENET UNTUK PENGENALAN WAJAH BERMASKER

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Penilaian

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The emergence of the COVID-19 virus has made facial recognition systems less effective in recognizing faces with masks. To overcome this, the author use the method proposed by Fadi Boutros named Unmasked Embedding Model (EUM) and the Self-restraint Triplet Loss loss function to improve the accuracy of the FaceNet facial recognition model. In this study, the accuracy level of the EUM model using the KomNet embedding dataset extracted using a FaceNet model that is not trained to recognize masked faces has an accuracy value of 0.6974 and a loss value of 1.256. Meanwhile, the FaceNet model that is trained to recognize masked faces gets an accuracy value of 0.7763 and a loss value of 0.7293.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207004270	T79993	T799932022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T799932022

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

xvii, 65 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.650 7

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jurusan Teknik Informatika
Jaringan Komunikasi Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

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