Abstract
Aiming at the problems of poor representation ability and less feature data when traditional expression recognition methods are applied to intelligent applications, an expression recognition method based on improved VGG16 network is proposed. Firstly, the VGG16 network is improved by using large convolution kernel instead of small convolution kernel and reducing some fully connected layers to reduce the complexity and parameters of the model. Then, the high-dimensional abstract feature data output by the improved VGG16 is input into the convolution neural network (CNN) for training, so as to output the expression types with high accuracy. Finally, the expression recognition method combined with the improved VGG16 and CNN model is applied to the human-computer interaction of the NAO robot. The robot makes different interactive actions according to different expressions. The experimental results based on CK + dataset show that the improved VGG16 network has strong supervised learning ability. It can extract features well for different expression types, and its overall recognition accuracy is close to 90%. Through multiple tests, the interactive results show that the robot can stably recognize emotions and make corresponding action interactions.
Highlights
Different facial expressions can reflect people’s emotional and psychological changes in different situations. erefore, expression recognition has very important research significance and practical application value for the study of human behavior and psychological activities
Chen et al [13] proposed a simplified face clipping and rotation strategy combined with the image recognition method of convolutional neural network (CNN), which ensures the richness of data while extracting facial features and considers the accuracy and richness of expression recognition, but the training time for some complex expressions is long
In view of the low recognition accuracy of most existing expression recognition methods, this paper proposes an expression recognition method using the improved VGG16 network model, which improves the overall effect of expression recognition on the basis of taking into account the accuracy and recognition efficiency
Summary
Different facial expressions can reflect people’s emotional and psychological changes in different situations. erefore, expression recognition has very important research significance and practical application value for the study of human behavior and psychological activities. Erefore, expression recognition has very important research significance and practical application value for the study of human behavior and psychological activities. With the rapid development of computer vision, the rise of deep learning, the improvement of machine learning, and other related theoretical systems, facial expression, as a bridge of human-computer interaction, has attracted the attention of researchers at home and abroad. With the development of deep learning, FER has gradually become one of the hot technologies in the field of human-computer interaction technology. With the continuous upgrading of computer technology, the improvement of computing power and communication ability has promoted the development of machine learning, especially, deep learning has made outstanding contributions to facial expression recognition [7, 8]. Journal of Robotics efficiency. erefore, this paper proposes an expression recognition method based on the improved VGG16 network model
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