Optimal deep transfer learning based ethnicity recognition on face images

Abstract

In recent times, deep learning driven face image analysis has gained significant interest among several application areas like surveillance, security, biometrics, etc. The facial analysis intends to compute facial soft biometrics like ethnicity, expression, identification, age, gender, and so on. Among several biometrics, ethnicity recognition remains a hot research area. Recent advancements in computer vision (CV) and artificial intelligence (AI) models form the basis of an effective design of ethnicity recognition models. With this motivation, this paper introduces a novel Harris Hawks optimization with deep transfer learning based fusion model for face ethnicity recognition (HHODTLF-FER) model. The proposed HHODTLF-FER model is to determine the different kinds of ethnicity for applied facial images. A fusion of three pre-trained DL models, namely VGG16, Inception v3, and capsule networks (CapsNet) models, are employed. In addition, bidirectional long short term memory (BiLSTM) model is applied for ethnicity recognition and Classification. Finally, HHO algorithm is utilized to fine tune the hyperparameters contained in the BiLSTM model, showing the novelty of the work. In order to ensure the improved recognition performance of the HHODTLF-FER model, a wide ranging experimental analysis is performed using benchmark databases. The comprehensive comparative study highlighted the promising performance of the HHODTLF-FER model over the other approaches. • To develop an ethnicity recognition on face images using harris hawks optimization basd deep transfer learning. • To improve the classification performance, bidirectional long short term memory (BiLSTM) model is applied for ethnicity recognition and Classification. • To improve the classification performance, HHO algorithm is utilized to fine tune the hyperparameters contained in the BiLSTM model, showing the novelty of the work.