Face Expression Recognition on Uncertainty-Based Robust Sample Selection Strategy  

作　　者：Yuqi Wang Wei Jiang 

机构地区：[1]School of Information Engineering,North China University of Water Resources and Electric Power,Zhengzhou 450046,China

出　　处：《Journal of Electronic Research and Application》2025年第2期211-215,共5页电子研究与应用

摘　　要：In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of annotators.Tracking the training history reveals that misclassified samples often exhibit high confidence and excessive uncertainty in the early stages of training.To address this issue,we propose an uncertainty-based robust sample selection strategy,which combines confidence error with RandAugment to improve image diversity,effectively reducing overfitting caused by uncertain samples during deep learning model training.To validate the effectiveness of the proposed method,extensive experiments were conducted on FER public benchmarks.The accuracy obtained were 89.08%on RAF-DB,63.12%on AffectNet,and 88.73%on FERPlus.

关 键 词：Facial expression recognition UNCERTAINTY Sample selection strategy 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]