Image-Based Face Recognition Techniques Used in Disease Detection Approaches: A Survey

Ghalib Ahmed Salman; Inas Jawad Kadhim; Ahmed Husham Al-Badri

doi:10.51173/jt.v6i1.1966

Authors

Ghalib Ahmed Salman Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
Inas Jawad Kadhim Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
Ahmed Husham Al-Badri Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

DOI:

https://doi.org/10.51173/jt.v6i1.1966

Keywords:

Face Recognition, Disease Detection, Artificial Intelligence, Convolutional‎ Neural Network, Face Disorders

Abstract

Facial diseases lead to noticeable changes on the human face, and some of them extend beyond internal effects or organ-based disorders. Indeed, certain types of facial diseases result in visually noticeable abnormalities on the human face. These alterations in facial patterns can serve as potential indicators for corresponding diseases, particularly in the fields of endocrinology and metabolism, Muscles-Nervous disorders, Chromosomes, and Genetic disorders, among others. Technologies used in Face Recognition (FR) have been developed over the past few decades; however, only a limited amount of research has been applied in recent years to FR-based disease detection for clinical purposes. FR applications relying on Artificial Neural Network (ANN) techniques have recorded higher accuracy rates in diagnosing facial diseases. This field of recognition holds promising potential for optimizing facial diagnosis approaches and supporting medical staff in evaluating detections. In practice, only a few research ideas have been translated into medical products, emphasizing the need to identify and integrate future applications. As a primary focus, this paper centers on the key applications and technologies for detecting various types of facial diseases, along with a discussion of prospects.

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Author Biographies

Ghalib Ahmed Salman, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.

Ghalib Ahmed Salman received his B.Sc in Statistics and computers from Baghdad University, Iraq in 1993, and his M.Sc. degree from University of Technology in Image security, Iraq in 2002. He received his PhD in Image recognition from University Technology of Malaysia, Malaysia in 2015. Previously, he was the manager of Digital Society Center-Iraqi Ministry of Science and Technology, and currently he works as a lecturer in the Middle Technical University. He has 16 published papers in image processing. He received the medal of Active Member in ARID Scientific ltd organization.

Inas Jawad Kadhim, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.

Inas Jawad Kadhim received her B.Sc. and M.Sc. degrees in Electrical and Computer Engineering from the University of Technology, Baghdad, Iraq in 2003 and 2005, respectively. She received her Ph.D. degree in Electrical and Computer Engineering from the University of Wollongong, Australia, in 2020. Currently, she works as a lecturer at the Electrical Engineering Technical College, Middle Technical University, in Baghdad, Iraq. Her research interests primarily focus on information hiding and image processing

Ahmed Husham Al-Badri, Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

Ahmed Husham Al-Badri received a Ph.D. degree from Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia in 2023 and an M.Sc. degree in Computer Science from the School of Computing, Faculty of Engineering at Universiti Teknologi Malaysia (UTM). He earned his master's degree at Universiti Teknologi Malaysia in 2014. His main research interests include Artificial Intelligence, Computer Vision, Image Processing, Machine Learning, and Object Detection.

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