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Accurate Facial Ethnicity Classification Using Artificial Neural Networks Trained with Galactic Swarm Optimization Algorithm

  • Chhandak Bagchi
  • D. Geraldine Bessie Amali
  • M. Dinakaran
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 862)

Abstract

Facial images convey important demographic information such as ethnicity and gender. In this paper, machine learning approach is taken to solve the ethnicity classification problem. Artificial neural networks trained by state of the art optimization algorithms are used to classify faces as Caucasian or non-Caucasian based on the color of the skin. A feedforward neural network is trained using Galactic Swarm Optimization (GSO) algorithm which gives superior performance to other training algorithms such as backpropagation and Particle Swarm Optimization (PSO) which have been used earlier. In this paper, the RGB values of the skin are taken as inputs to the neural network. Each pixel of the image will be classified according to their RGB values and the class having the maximum number of pixels will be the output. Simulation results indicate that the neural network trained with GSO gives a more accurate classification and converges faster than the other state of the art optimization algorithms.

Keywords

Artificial neural network Galactic swarm optimization Backpropagation Image segmentation Particle swarm optimization 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Chhandak Bagchi
    • 1
  • D. Geraldine Bessie Amali
    • 1
  • M. Dinakaran
    • 2
  1. 1.School of Computer Science and EngineeringVellore Institute of TechnologyVelloreIndia
  2. 2.School of Information TechnologyVellore Institute of TechnologyVelloreIndia

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