Face Recognition of Indonesia’s Top Government Officials Using Deep Convolutional Neural Network

  • Umar Aditiawarman Universitas Nusa Putra
  • Dimas Erlangga Universitas Nusa Putra
  • Teddy Mantoro Universitas Sampoerna
  • Lutfil Khakim Universitas Nusa Putra
DOI: https://doi.org/10.29207/resti.v7i1.4437
Keywords: Face recognition, Deep Convolutional Neural Network, FaceNet, Haar Cascaded Classifier

Abstract

Facial recognition is a part of Computer Vision that is used to get facial coordinates from an image. Many algorithms have been developed to support Facial Detection such as Cascade Face Detection using Haar-Like features and AdaBoost to classify its Cascade and Convolutional Neural Network (CNN). Face recognition in this study uses the Deep Convolutional Neural Network (DCNN) method, and the output of this method is the measurement value of the face. In the model training process, Triplet Loss from Triplet Network Deep Metric Learning is used to get good face grouping results. The value of this face measurement will then be measured using the Euclidean distance calculation to determine the similarity of the input face from the dataset. This Research is using 6 images of Government officers in Indonesia to determine the accuracy of the model when there is a new picture of these officers inputted into the training machine. The result provides a 94% accuracy level with a variety of face positions and levels of brightness.

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Published
2023-02-02
How to Cite
Umar Aditiawarman, Dimas Erlangga, Teddy Mantoro, & Lutfil Khakim. (2023). Face Recognition of Indonesia’s Top Government Officials Using Deep Convolutional Neural Network. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(1), 113 - 119. https://doi.org/10.29207/resti.v7i1.4437
Issue
Vol 7 No 1 (2023): February 2023
Section
Information Systems Engineering Articles

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