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A Face Emotion Recognition Method Using Convolutional Neural Network and Image Edge Computing
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DOI：10.1109/ACCESS.2019.2949741.png)
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Abstract
En 中文
To avoid the complex process of explicit feature extraction in traditional facial expression recognition, a face expression recognition method based on a convolutional neural network (CNN) and an image edge detection is proposed. Firstly, the facial expression image is normalized, and the edge of each layer of the image is extracted in the convolution process. The extracted edge information is superimposed on each feature image to preserve the edge structure information of the texture image. Then, the dimensionality reduction of the extracted implicit features is processed by the maximum pooling method. Finally, the expression of the test sample image is classified and recognized by using a Softmax classifier. To verify the robustness of this method for facial expression recognition under a complex background, a simulation experiment is designed by scientifically mixing the Fer-2013 facial expression database with the LFW data set. The experimental results show that the proposed algorithm can achieve an average recognition rate of 88.56 with fewer iterations, and the training speed on the training set is about 1.5 times faster than that on the contrast algorithm.
Keywords:
Feature extraction
Face recognition
Image edge detection
Face
Eigenvalues and eigenfunctions
Emotion recognition
Face expression recognition
convolutional neural network
edge computing
deep learning
image edge detection
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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Cited Papers
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IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
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