摘要
针对目前人脸表情识别大多采用基于深度学习的端到端特征提取及分类方法的现象,提出了一种新的深度模型优化方法。基于ResNet18残差网络架构和正则化思想,提出了联合正则化策略,即将过滤器响应正则化和批量正则化、实例正则化和组正则化、组正则化和批量正则化分别嵌入网络之中,平衡和改善特征数据分布,弥补单一正则化的缺点,提升模型性能。在2个公开数据集FER2013和CK+进行了验证和测试,最高准确率分别达到了73.558%和94.9%,实验结果表明,联合正则化策略提高了基础网络的性能,其表现优于诸多当前较新的人脸表情识别方法。
As for that end-to-end feature extraction and classification based on deep learning often used in facial expression recognition,a new method of depth model optimization has been proposed.This paper proposes the joint optimization strategies learned from ResNet18 residual network and normalization ideas,that is,filter response normalization and batch normalization,instance normalization and group normalization,as well as group normalization and batch normalization were embedded in the network,respectively,to balance and improve the distribution of feature data,make up for the shortcomings of single regularization,and improve model performance.The validation and test were carried out on the two public datasets FER2013 and CK+,and the highest accuracy rates are 73.558%and 94.9%,respectively.The experimental results indicate that the joint optimization strategy enhances the performance of the basic network,which is better than most of the latest facial expression recognition methods.
作者
兰凌强
李欣
刘淇缘
卢树华
LAN Lingqiang;LI Xin;LIU Qiyuan;LU Shuhua(College of Police Information Technology and Cyber Security,People's Public Security University of China,Beijing 102600,China)
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2020年第9期1797-1806,共10页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家重点研发计划(2016YFC0801005)
中央高校基本科研业务费专项资金(2019JKF225)。
关键词
表情识别
联合正则化策略
过滤器响应正则化
批量正则化
组正则化
expression recognition
joint strategy
filter response normalization
batch normalization
group normalization
