Face expression recognition based on NGO-BILSTM model - Zhong, Jiarui; Chen, Tangxian; Yi, Liuhan (Frontiers in Neurorobotics, 2023)
Exploring facial expression recognition is to provide technical support for artificial intelligence emotional expression. This paper firstly introduces the optimization principle of the Northern Goshawk Optimization(NGO) algorithm and the long and short-term memory(LSTM) neural network and uses the bidirectional data transfer of neurons of LSTM for forward and reverse data transfer, which in turn leads to the principle of bidirectional long and short term memory(BILSTM) neural network. Then it is illustrated that BILSTM can tap the intrinsic connection between the current moment data and the past and future data and then improve the model testing accuracy and data utilization efficiency. The NGO algorithm is used to optimize the hyperparameters of the BILSTM, and the weights of the BILSTM are updated using the cross-entropy loss and Adam optimizer to construct a facial expression recognition model based on the NGO-BILSTM. Finally, for this model, the performance is evaluated using the facial expression recognition dataset. The results show that the recognition accuracy of the model in this paper reaches 51.29% and 78.75% on the FER2013 and FERPlus datasets, respectively, which is 6.15% and 12.12% higher than that of the traditional VGG16 network, respectively. This shows that the NGO-BILSTM model proposed in this chapter has better face expression recognition and can be effectively applied to facial ex-pression recognition.