Using Convolutional Neural Networks to Detect Races in Human Faces using MATLAB Programming

Using Convolutional Neural Networks to Detect Races in Human Faces using MATLAB Programming
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-6
Year of Publication : 2023
Author : I Indriyani, Ida Ayu Dwi Giriantari, Made Sudarma, I Made Oka Widyantara
DOI : 10.14445/22315381/IJETT-V71I6P208

How to Cite?

I Indriyani, Ida Ayu Dwi Giriantari, Made Sudarma, I Made Oka Widyantara, "Using Convolutional Neural Networks to Detect Races in Human Faces using MATLAB Programming," International Journal of Engineering Trends and Technology, vol. 71, no. 6, pp. 66-74, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I6P208

Abstract
Information on race for several purposes is needed, such as the application in determining the forensic problems associated with race. Furthermore, it is important to note that there are four major racial groups in Indonesia, including the Malay Mongolian, Weddoid, Papua Melanesoid, and the Mixed. The objective of this study is to investigate the possibility of using MATLAB programming to build a convolutional neural network (CNN) structure to recognize an image of the human face and detect its race. The model used in this research is a pretrained existing network structure known as ResNet50, commonly used for image recognition. The four races were further trained to determine the accuracy of the CNN. Both image data processing and network training are carried out using simulations based on MATLAB programming. The percentage of detection accuracy for Mongoloid, Weddoid, Papua Melanesoid, and Mixed races against all available images was 87.2%, 98.1%, 95.3%, and 93.2%, respectively. CNNs accurately detect races and can be easily built with MATLAB programming. The detection accuracy was observed to be very good for all the races except for the Malay-Mongoloid, which was only quite good and found to have the lowest value.

Keywords
Convolutional Neural Network, MATLAB, Race, Rasnet50.

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