Face expression recognition based on NGO-BILSTM model

Frontiers in Neurorobotics

Neuroscience

Liuhan Yi¹  Jiarui Zhong¹  Tangxian Chen² 

[1] College of Electrical Engineering and New Energy, China Three Gorges University, Yichang, China;null;

关键词: northern goshawk algorithm;    NGO-BILSTM model;    face recognition;    facial expression;    hyperparameter optimization;   

DOI  :  10.3389/fnbot.2023.1155038

 received in 2023-01-31, accepted in 2023-03-03,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionFacial expression recognition has always been a hot topic in computer vision and artificial intelligence. In recent years, deep learning models have achieved good results in accurately recognizing facial expressions. BILSTM network is such a model. However, the BILSTM network's performance depends largely on its hyperparameters, which is a challenge for optimization.MethodsIn this paper, a Northern Goshawk optimization (NGO) algorithm is proposed to optimize the hyperparameters of BILSTM network for facial expression recognition. The proposed methods were evaluated and compared with other methods on the FER2013, FERplus and RAF-DB datasets, taking into account factors such as cultural background, race and gender.ResultsThe results show that the recognition accuracy of the model on FER2013 and FERPlus data sets is much higher than that of the traditional VGG16 network. The recognition accuracy is 89.72% on the RAF-DB dataset, which is 5.45, 9.63, 7.36, and 3.18% higher than that of the proposed facial expression recognition algorithms DLP-CNN, gACNN, pACNN, and LDL-ALSG in recent 2 years, respectively.DiscussionIn conclusion, NGO algorithm effectively optimized the hyperparameters of BILSTM network, improved the performance of facial expression recognition, and provided a new method for the hyperparameter optimization of BILSTM network for facial expression recognition.

【 授权许可】

Unknown   
Copyright © 2023 Zhong, Chen and Yi.