A facial expression recognition method based on Convolutional Neural Network
DOI:
https://doi.org/10.54097/fcis.v2i1.3178Keywords:
CNN, FER, Jaffe, CK , FER2013Abstract
This project is the implementation method of a simple static facial expression recognition (FER) project. Under the environment of Python3, the deep learning model is used to compare with the traditional model. Finally, CNN (Convolutional Neural Networks) is actually used to construct the entire system, and model evaluation is carried out on the three FER datasets FER2013, JAFFE and CK+. The project's main functions include "get a picture of a person's face" and "recognize expressions". The principles of the above functions include the following: the establishment of neural network structure, the acquisition of data sets, the model training based on data sets, the use of OpenCV to obtain the face, and the use of the model to recognize the expression. The experimental results reproduce the simple deep learning model to realize FER and verify the different effects of different data sets on the results. Face recognition has been widely used in all aspects of life, but different purposes need different models and data collection methods, and the laboratory collection cost is high, and the amount of data is limited, so this project also discusses the data set selection methods under different purposes.
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References
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