Research Area:  Machine Learning
Automatic recognition of facial expression images is a challenge for computer due to variation of expression, background, position and label noise. The paper propose a new method for static facial expression recognition. Main process is to perform experiments by FER-2013 dataset, the primary mission is using our CNN model to classify a set of static images into 7 basic emotions and then achieve effective classification automatically. The two preprocessing of the faces picture have enhanced the effect of the picture for recognition. First, FER datasets are preprocessed with standard histogram eqialization. Then we employ ImageDataGenerator to deviate and rotate the facial image to enhance model robustness. Finally, the result of softmax activation function (also known as multinomial logistic regression) is stacked by SVM. The result of softmax activation function + SVM is better than softmax activation function. The accuracy of facial expression recognition achieve 68.79% on the test set.
Keywords:  
Facial Expression Recognition
Deep Learning
Machine Learning
Author(s) Name:  XuMing Wang , Jin Huang , Jia Zhu , Min Yang , Fen Yang
Journal name:  
Conferrence name:  ICIMCS -18: Proceedings of the 10th International Conference on Internet Multimedia Computing and Service
Publisher name:  ACM
DOI:  10.1145/3240876.3240908
Volume Information:  Article No.: 10,Pages 1–4
Paper Link:   https://dl.acm.org/doi/abs/10.1145/3240876.3240908