Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Aiming at the insufficient feature extraction in the expression feature extraction stage of traditional convolutional neural network and the misclassification of mislabeled samples, an expression recognition and robot intelligent interaction method using deep learning is proposed. First, in image preprocessing, the dimension of the color image is reduced by image gray adjustment to reduce the amount of calculation, the shadow interference is eliminated by the average method, and the image is enhanced by histogram equalization. Second, multichannel convolution is used to replace the single convolution size in the second convolution layer in AlexNet, the Global Average Pooling layer is introduced to replace the fully connected layer, and Batch Normalization is introduced to improve the feature extraction ability of the model and avoid gradient explosion. Finally, the Focal Loss is improved by setting the probability threshold to avoid the impact of mislabeling samples on the classification performance of the model. The experimental results show that the recognition accuracy of the model on FER2013 data set is 98.36%. The effectiveness of the algorithm is verified on the intelligent interactive system of service robot based on expression recognition. Compared with other expression recognition methods, the proposed method can extract more expression features and recognize facial expression more accurately.
Aiming at the insufficient feature extraction in the expression feature extraction stage of traditional convolutional neural network and the misclassification of mislabeled samples, an expression recognition and robot intelligent interaction method using deep learning is proposed. First, in image preprocessing, the dimension of the color image is reduced by image gray adjustment to reduce the amount of calculation, the shadow interference is eliminated by the average method, and the image is enhanced by histogram equalization. Second, multichannel convolution is used to replace the single convolution size in the second convolution layer in AlexNet, the Global Average Pooling layer is introduced to replace the fully connected layer, and Batch Normalization is introduced to improve the feature extraction ability of the model and avoid gradient explosion. Finally, the Focal Loss is improved by setting the probability threshold to avoid the impact of mislabeling samples on the classification performance of the model. The experimental results show that the recognition accuracy of the model on FER2013 data set is 98.36%. The effectiveness of the algorithm is verified on the intelligent interactive system of service robot based on expression recognition. Compared with other expression recognition methods, the proposed method can extract more expression features and recognize facial expression more accurately.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.