Computer Vision for Automated Facial Characteristics Detection

Yasser Emad Salman; Ammar Yahya Daeef; Asanka G. Perera; Ali Al-Naji

doi:10.51173/eetj.v1i1.5

Authors

Yasser Emad Salman Electrical Engineering Technical College, Middle Technical University, Baghdad 10022, Iraq
Ammar Yahya Daeef Technical Institute for Administration, Middle Technical University, Baghdad 10074, Iraq
Asanka G. Perera School of Engineering, University of Southern Queensland, Springfield 4300 QLD, Australia
Ali Al-Naji School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia

DOI:

https://doi.org/10.51173/eetj.v1i1.5

Keywords:

Pterygium, Deep Learning, Xception, ViT, VGG16, Gender Recognition, Facial Characteristics, Bell's Paralysis

Abstract

Diagnosing diseases in their early stages allows people to see a specialist doctor before the disease reaches advanced stages and avoid any future complications. The use of a real-time imaging system to reliably give various information about the patient's condition, including gender classification, pterygium, and Bell's paralysis, contributes to reducing the duration of diagnosis and human errors. This study focuses on the use of three artificial intelligence algorithms based on deep learning, namely Visual Geometry Group (VGG16), Vision Transformer (ViT), and Xception, and evaluates their performance in detecting gender, pterygium, and Bell's paralysis. VIT has the highest overall performance results from the rest of the algorithms.

Author Biographies

Ammar Yahya Daeef, Technical Institute for Administration, Middle Technical University, Baghdad 10074, Iraq

Ammar Yahyad Daeef earned his Bachelor of Engineering in Computer Engineering Techniques from the Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq in 2006. He later obtained his Master's degree in Engineering in Computer Engineering Techniques from the same institution in 2008. He completed his Ph.D. in Computer Engineering at University Malaysia Perlis (UniMAP), Malaysia in 2017. Currently, Ammar serves as a lecturer in the Department of Computer Systems and holds the position of Vice-Dean for Scientific Affairs at the Technical Institute for Administration. His research interests encompass hardware design, embedded systems, artificial intelligence (AI), machine learning, malware data science, and cybersecurity solutions.

Asanka G. Perera, School of Engineering, University of Southern Queensland, Springfield 4300 QLD, Australia

Dr. Asanka is a lecturer at the University of Southern Queensland. His research is focused on robotics, mechatronics, drones, machine learning, computer vision and signal processing. Prior to joining UniSQ, Asanka was a postdoctoral researcher at UNSW Canberra, Central Queensland University and University of South Australia. He has worked with several industry partners

Ali Al-Naji, School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia

Ali Abdulelah Al-Naji received the bachelor of Engineering in Medical Instrumentation Techniques from the Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq (2005), master degree in Electrical & Electronic Engineering from the University of Technology, Baghdad, Iraq (2008) and Ph.D. degree in the Electrical & Information Engineering, University of South Australia (UniSA), Australia (2018). Ali is now an associate professor in the Department of Medical Instrumentation Techniques Engineering, and he holds the position of vice-dean for administrative and financial affairs in the Electrical Engineering Technical College. Ali is also with the School of Engineering, University of South Australia (Mawson Lakes, SA 5095, Australia) as an adjunct associate professor since 2022. He is a member of the Institute of Electrical and Electronics Engineers IEEE (2017), Engineers Australia EA (EA ID: 6099558) and the International Association of Engineers IAENG (membership no.: 212257). His research interests include biomedical instrumentation and sensors, health-care applications, computer vision systems, and microcontroller applications.

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