PERBANDINGAN KINERJA PENGENALAN WAJAH MENGGUNAKAN METODE FACENET PYTORCH DAN KERAS FACENET

FITRIADY, DEDY and Samsuryadi, Samsuryadi and Primanita, Anggina (2024) PERBANDINGAN KINERJA PENGENALAN WAJAH MENGGUNAKAN METODE FACENET PYTORCH DAN KERAS FACENET. Masters thesis, Sriwijaya University.

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Abstract

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

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Pengenalan Wajah
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Dedy Fitriady
Date Deposited:	16 Jan 2025 08:51
Last Modified:	16 Jan 2025 08:51
URI:	http://repository.unsri.ac.id/id/eprint/164748

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