权重推断与标签平滑的轻量级人脸表情识别OACSTPCD

Aiming at the problems of insufficient feature extraction of facial expressions by lightweight networks in complex environments,redundant model parameters,and the inability of single-label datasets to effectively describe the ambiguous expressions caused by complex emotional tendencies,a facial expression recognition method combining improved Shuf-fleNet and label smoothing learning is proposed.Firstly,by analyzing and tailoring the original network,the improved and more compact K5_Light_ShuffleNet is obtained,which not only optimizes the network parameters,but also improves the representation ability of the model.Secondly,in order to enhance the ability of the model to extract local detail fea-tures of facial expression images and suppress non-expression features,a designed lightweight channel-space key weight inference module is embedded in the model.Finally,through the label smoothing learning method,the learning of the net-work is supervised by the soft label distribution without introducing additional information,so as to reduce the adverse effect on the recognition performance due to ambiguous expressions.The experimental results show that the recognition accuracy rates of 86.91%,61.80%and 58.75%are achieved on the RAF-DB,AffectNet-7 and AffectNet-8 datasets,respec-tively.Compared with other current facial expression recognition methods,the recognition rate has been improved to a certain extent.At the same time,the amount of model parameters and FLOPs are kept at a low level,which is beneficial to its practical application.