Skripsi

SISTEM DETEKSI DAN PENGENALAN WAJAH SEBAGAI SECURITY SYSTEM BERBASIS HYBRID DEEP LEARNING ALGORITHM.

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Penilaian

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Currently, the development of security system technology has used a combination of biometrics and artificial intelligence through face detection and recognition systems. Although many studies related to face detection have been carried out, face detection technology still faces many challenges in practical application, where face detection is currently still carried out at the close range and relies heavily on facial features so that the accuracy obtained is highly dependent on the accuracy of feature selection. Therefore, this study developed a real-time face detection and recognition system using a hybrid deep learning algorithm. This system used the performance of the convolutional neural network (CNN) with the FaceNet method and the deep belief network (DBN) algorithm. The objects used in this study were 9 faces, where 6 people become data practitioners who can access the room and 3 people become data unknowns who do not have the right to access the room. The total number of data obtained is 18000 data. Furthermore, the data was trained using FaceNet and DBN, where the best training model was a model that used a learning rate of 0.1 with a loss value of 0.971691. Furthermore, the model was tested on offline and real-time test data. Faces recorded by CCTV were detected by the system using the FaceNet model that has carried out the embedding process and the system were able to recognize faces using the trained DBN model. The test results showed the system can detect and recognize faces in still images offline with a probability of 100%. Meanwhile, in real-time test results, the system detected faces and send the information to users directly via telegram media

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207003182	T75117	T751172022	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

2207003182

Publisher

Indralaya : Prodi Teknik Elektro, Fakultas Teknik Universitas Sriwijaya., 2022

Collation

xvi, 37 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

621.382 07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik Komunikasi
Teknik elektro
signal processing/sinyal

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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