Visible to the public Facial expression recognition based on the ensemble learning of CNNs

TitleFacial expression recognition based on the ensemble learning of CNNs
Publication TypeConference Paper
Year of Publication2020
AuthorsJia, C., Li, C. L., Ying, Z.
Conference Name2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)
Keywordsbody language, CNN, convolutional neural nets, convolutional neural network ensemble learning, convolutional neural networks, emotion recognition, Ensemble Learning, face recognition, facial expression recognition, facial expression recognition method, facial recognition, feature extraction, FER2013 dataset, high-performance facial recognition, Human Behavior, image classification, image recognition, learning (artificial intelligence), Metrics, psychological state, psychology, pubcrawl, resilience, Resiliency, Support vector machines, SVM classifier, Training

As a part of body language, facial expression is a psychological state that reflects the current emotional state of the person. Recognition of facial expressions can help to understand others and enhance communication with others. We propose a facial expression recognition method based on convolutional neural network ensemble learning in this paper. Our model is composed of three sub-networks, and uses the SVM classifier to Integrate the output of the three networks to get the final result. The recognition accuracy of the model's expression on the FER2013 dataset reached 71.27%. The results show that the method has high test accuracy and short prediction time, and can realize real-time, high-performance facial recognition.

Citation Keyjia_facial_2020