Intelligent System for Face Recognition on Medically Altered Surgical Images using Machine Learning Algorithms

Authors

  • Shiji S. K. Research Scholar, Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Tamilnadu, India
  • S. H. Krishnaveni Associate Professor, Department of Computer Science and Engineering, Baselios Mathews II College of Engineering, Kollam, India

Keywords:

Face Reconstruction, plastic surgery, granular computing, Extended Uniform Circular Local Binary Pattern, Harmony Search Algorithm and KNN

Abstract

Over the last two decades, there has been a significant increase in the number of plastic surgery procedures performed on the face, ranging from minimally invasive treatments to more extensive surgeries. This trend, which carries social and cultural significance, is expected to continue growing as the cost of these procedures decreases and the desire for physical enhancement becomes more widespread. Consequently, the field of face recognition research faces a new challenge: developing methods that can accurately identify individuals even if their appearance has been altered through facial surgery. Despite previous efforts to overcome challenges such as variations in pose, expression, illumination, aging, and disguise, the rise in plastic surgery necessitates further advancements in this area. While plastic surgery is viewed as a challenging topic of study, there has been little theoretical or experimental research conducted on the subject. The objective of this article is to examine the impact of plastic surgery on facial recognition algorithms. In this paper, the performance of face reconstruction is performed Extended Uniform Circular Local Binary Pattern (EUCLBP), Harmony Search Algorithm and KNN classifier. An open dataset of 1800 before and after images from 900 human faces undergoing plastic surgery was used to test all algorithms. Each individual had two front-facing images taken with appropriate lighting and neutral expressions - one before surgery and one after. The results consistently demonstrated better performance and higher accuracy in identification compared to the current algorithm.

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Published

01.07.2023

How to Cite

S. K., S. ., & Krishnaveni, S. H. . (2023). Intelligent System for Face Recognition on Medically Altered Surgical Images using Machine Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 621–630. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2999

Issue

Vol. 11 No. 7s (2023): Advancements in Machine Learning for Computer Science and Decision Support Systems

Section

Research Article

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