AUTOMATED ETHNICITY RECOGNITION USING EQUILIBRIUM OPTIMIZER WITH MACHINE LEARNING ON FACIAL IMAGES

In recent times, computer vision related face image analysis has gained signifi-cant attention in various applications namely biometrics, surveillance, security, data retrieval, informatics, etc. The main objective of the facial analysis is to ex-tract 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 clas-sification 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 vec-tors. For ethnicity recognition, the presented model uses long short-term memory method. To improve the recognition performance, the FIER-EOML technique uti-lizes EO algorithm for hyperparameter tuning process. The performance valida-tion 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.