Face identification using multi-layer perceptron and convolutional neural network

Kartika Fithriasari, Ulfa Siti Nuraini

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Algorithm that is often used for image processing is machine learning. One of machine learning is Neural Network, which has additional layer called Multi-Layer Perceptron (MLP). Besides, other technique is Convolutional Neural Network (CNN). In this paper, we compare MLP and CNN methods carried out in image processing for face identification case study. We used preprocessing to change RGB to grayscale and normalize it. We proposed to use image augmentation to get more data without taking some images again. Lack of images can make overfitting so image augmentation may reduce it. For training, we used Adam optimizer. It is more efficient to train images that have complicated patterns. The conclusion is that CNN method has better results, due to its higher accuracy, precision, sensitivity, and Fscore than MLP method.

Original languageEnglish
Pages (from-to)157-164
Number of pages8
JournalICIC Express Letters
Volume15
Issue number2
DOIs
Publication statusPublished - Feb 2021

Keywords

  • Convolutional neural network
  • Face
  • Image
  • Multi-layer perceptron

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