Expression Recognition Using Improved AlexNet Network in Robot Intelligent Interactive System

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 pr...

Full description

Saved in:
Bibliographic Details
Published inJournal of Robotics Vol. 2022; pp. 1 - 9
Main Authors Zhao, Yifeng, Chen, Deyun
Format Journal Article
LanguageEnglish
Published New York Hindawi 08.02.2022
Hindawi Limited
Subjects
Accuracy
Algorithms
Analysis
Artificial intelligence
Artificial neural networks
Classification
Color imagery
Deep learning
Digital signatures
Emotions
Equalization
Feature extraction
Feature recognition
Histograms
Human-computer interaction
Image enhancement
Interactive systems
Machine learning
Model accuracy
Neural networks
R&D
Research & development
Robotics
Robotics industry
Robots
Service robots
China
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:1687-9600
1687-9619
DOI:10.1155/2022/4969883