Facial expression recognition based on semi-supervised contrast learning
In this paper,a method of a facial expression recognition based on semi-supervised contrast learning is proposed.Based on Resnet-18 residual network,the image preprocessing module is added to process the input expression pictures.The advantage of semi-supervised learning is fully utilized,and the unlabeled data is combined with labeled data to better describe the synthesized data in the input space.The network is also used for comparative learning methods to expand the class spacing when clustering,while new data is automatically labeled in the embedded space by the class centroid distance.The method was tested on RAF-DB field facial expression recognition datasets,in which 2 000 labeled training sets had a test accuracy of 81.37%and 4 000 labeled training sets had a test accuracy of 83.63%.