Improved Facial Expression Recognition Method Based on GAN

Abstract

Recognizing facial expressions accurately and effectively is of great significance to medical and other fields. Aiming at problem of low accuracy of face recognition in traditional methods, an improved facial expression recognition method is proposed. The proposed method conducts continuous confrontation training between the discriminator structure and the generator structure of the generative adversarial networks (GANs) to ensure enhanced extraction of image features of detected data set. Then, the high-accuracy recognition of facial expressions is realized. To reduce the amount of calculation, GAN generator is improved based on idea of residual network. The image is first reduced in dimension and then processed to ensure the high accuracy of the recognition method and improve real-time performance. Experimental part of the thesis uses JAFEE dataset, CK + dataset, and FER2013 dataset for simulation verification. The proposed recognition method shows obvious advantages in data sets of different sizes. The average recognition accuracy rates are 96.6%, 95.6%, and 72.8%, respectively. It proves that the method proposed has a generalization ability.

Exploring the Teaching Reform of Middle School Ideological and Political Classroom Based on the Background of Internet

“…In the smart classroom teachers can analyze the adaptability and acceptability of the teaching content through students' facial expressions, and Generative Adversarial Networks (GANs) are capable of generating consecutive multi-expression images from a single face expression image, making the trained expression classification network more targeted [22].…”

Facial Expression Recognition Using Data Augmentation and Transfer Learning

“…The method suggested by Wang [14], involves continual confrontation training between the generator and discriminator structures of Generative Adversarial Networks to enable improved extraction of visual characteristics from a detected input set. Then, high-accuracy face expression recognition is achieved.…”

“…Accuracy (%) Zahra et al [11] 65.97 Saleh et al [7] 74.45% Singh and Nasoz [9] 75.2% Wang [14] 72.8% Our 90% The normalized confusion matrix is demonstrated in Table 4.…”

Generative Adversarial Networks for Facial Expression Recognition in the Wild

“…The persistent confrontation training among the generator structure and the discriminator structure would improve both the discriminator's identification ability and the accurate extraction of image features. These automatic features engineering or representation learning are suggested to indicate that the input comes from the training https:// journal.uob.edu.bh/ dataset [11]. In this context, the adversarial training process is repeated until a state of Nash equilibrium [12] is reached between the Generator and the Discriminator to achieve good images.…”