TY - JOUR
TI - Automated ethnicity recognition using equilibrium optimizer with machine learning on facial images
AU - Asiri Yousef
AU - Alhabeeb Abdullah
AU - Mashraqi Aisha M
AU - Algarni Abeer D
AU - Abdel-Khalek Sayed
JN - Thermal Science
PY - 2022
VL - 26
IS - 101
SP - 353
EP - 364
PT - Article
AB - In recent times, computer vision related face image analysis has gained  significant attention in various applications namely biometrics,  surveillance, security, data retrieval, informatics, etc. The main objective  of the facial analysis is to extract facial soft biometrics like  expression, identity, age, ethnicity, gender, etc. Of these, ethnicity  recognition is considered a hot search topic, a major part of community with  deep connections to many social and ecological concerns. The deep learning  and machine learning methods is merit for effective ethnicity  classification and recognition. This study develops a facial imaging based  ethnicity recognition using equilibrium optimizer with machine learning  (FIER-EOML) model. The goal of the FIER-EOML technique is to detect and  classify different kinds of ethnicities on facial images. To accomplish  this, the presented FIER-EOML technique applies an EfficientNet model to  generate a set of feature vectors. For ethnicity recognition, the presented  model uses long short-term memory method. To improve the recognition  performance, the FIER-EOML technique utilizes EO algorithm for  hyperparameter tuning process. The performance validation of the FIER-EOML  technique is tested on BUPT-GLOBALFACE dataset and the results are examined  under several measures. The comprehensive comparison study reported the  enhanced performance of the FIER-EOML technique over other recent  approaches.