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A novel genetic-artificial neural network based age estimation system.

Oluwasegun Oladipo1, Elijah Olusayo Omidiora2, Victor Chukwudi Osamor3

  • 1Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.

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Summary

This study introduces a new age estimation system for black faces using a genetic algorithm and artificial neural network (ANN). The developed system (LBGANN) shows improved accuracy compared to standard ANN methods.

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Area of Science:

  • Computer Science
  • Biometrics
  • Artificial Intelligence

Background:

  • Facial age estimation has practical applications in various detection scenarios.
  • Existing automatic age estimation systems lack focus on black facial features.
  • There is a need for specialized age estimation models for diverse populations.

Purpose of the Study:

  • To develop and evaluate a novel automatic age estimation system specifically for black faces.
  • To address the limitations of current systems in accurately estimating age in this demographic.
  • To compare the performance of the novel system against a standard artificial neural network approach.

Main Methods:

  • A hybrid approach combining a genetic algorithm with a back propagation (BP)-trained artificial neural network (ANN).
  • Utilized the local binary pattern (LBP) feature extraction technique.
  • Trained and tested the system using a predominantly black face image database.

Main Results:

  • The developed system, termed LBGANN, demonstrated superior performance.
  • LBGANN achieved a higher correct classification rate compared to the standard ANN system (LBANN).
  • The proposed method shows significant potential for accurate age estimation in black individuals.

Conclusions:

  • The novel LBGANN system effectively addresses the need for accurate age estimation in black faces.
  • The combination of genetic algorithms and ANNs with LBP features offers a promising solution.
  • This research contributes to more equitable and accurate biometric systems.