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Image of PERBANDINGAN KINERJA PENGENALAN WAJAH MENGGUNAKAN METODE FACENET PYTORCH DAN KERAS FACENET

Skripsi

PERBANDINGAN KINERJA PENGENALAN WAJAH MENGGUNAKAN METODE FACENET PYTORCH DAN KERAS FACENET

Fitriady, Dedy - Personal Name;

Penilaian

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Penilaian anda saat ini :  

Face recognition has become an important technology in various applications, but challenges arise when multiple faces need to be recognized simultaneously in a single image or video frame. This research develops a multiple face recognition system using the Multi-Task Cascaded Convolutional Neural Network (MTCNN) method for face detection, Facenet models with Pytorch and Keras frameworks for face recognition, and Support Vector Machine (SVM) for classification. This research compares the performance of Facenet Pytorch and Keras Facenet in terms of processing speed, memory usage efficiency, and recognition accuracy. Using a dataset of 1000 images taken from 10 different classes with training and testing data percentages of 70%:30% and 80%:20%, this research shows that Facenet Pytorch is faster and more efficient in memory usage. The average time required by Facenet Pytorch for the embedding process is 0.15 seconds per image, while Keras Facenet requires 0.86 seconds. Facenet Pytorch also uses less RAM, 384.19 MB lower than Keras Facenet. Although Facenet Pytorch uses 3% more CPU, its speed and memory efficiency make it more suitable for applications that require fast response and low memory usage. In system testing, Facenet Pytorch outperforms Keras Facenet with an average time of 10.549 seconds faster and more efficient memory usage of 13.98 MB, despite using 64.254% more CPU. Both models can accurately recognize faces, but Facenet Pytorch generally provides higher and more consistent confidence scores. This study concludes that Facenet Pytorch is more efficient and reliable in multiple face recognition, although it requires further optimization in CPU usage. Keywords: multi-face recognition, MTCNN, Facenet, SVM, Pytorch, Keras


Availability
Inventory Code Barcode Call Number Location Status
2507001256T164748T1647482024Central Library (Reference)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1647482024
Publisher
Indralaya : Prodi Magister Ilmu Komputer, Fakultas Ilmu Komputer, Universitas Sriwijaya., 2024
Collation
xiv, 95 hlm.; ilus.; tab.; 29 cm.
Language
Indonesia
ISBN/ISSN
-
Classification
004.07
Content Type
Text
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Ilmu Komputer
Specific Detail Info
-
Statement of Responsibility
MI
Other version/related

No other version available

File Attachment
  • PERBANDINGAN KINERJA PENGENALAN WAJAH MENGGUNAKAN METODE FACENET PYTORCH DAN KERAS FACENET
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