基于深度学习的人脸局部遮挡表情动态识别算法  

Dynamic Recognition Algorithm of Facial Partial Occlusion Expression Based on Deep Learning

作　　者：陈曦 蔡现龙 CHEN Xi;CAI Xianlong(School of Information Engineering,Xi'an Mingde Institute of Technology,Xi'an 710065,China)

机构地区：[1]西安明德理工学院信息工程学院,西安710065

出　　处：《吉林大学学报（信息科学版）》2024年第3期503-508,共6页Journal of Jilin University（Information Science Edition）

基　　金：陕西省教育厅科学研究计划基金资助项目(23JK0576)。

摘　　要：针对因人脸局部遮挡,导致表情动态特征较难提取和识别问题,提出一种基于深度学习的人脸局部遮挡表情动态识别算法。建立深度信念网络模型,将前一层输出值作为后一层输入值,设计特征堆叠单元,计算可见层中神经元的状态变量分布情况,根据面部五官间动态关联性,将可见层的状态值作为隐藏层的输入值求得隐藏神经元状态变量。将识别过程分为训练和正向传播2个步骤,输出特征变化规律,在正向传播过程中查找符合规律变化的像素点,求解该像素点权重,同时作为损失函数标准,比对面部多个位置的识别权重,约束识别率,完成人脸局部遮挡表情动态识别。实验数据证明,该方法能降低图像失真和细节丢失,提高图像分辨率,识别率高,针对不同局部遮挡情况均能完成高效识别。Aiming at the problem that it is difficult to extract and recognize the dynamic features of facial expression due to local occlusion,a dynamic recognition algorithm of facial expression with local occlusion based on deep learning is proposed,a deep belief network model is established,taking the output value of the previous layer as the input value of the next layer,a feature stacking unit is designed,the distribution of state variables of neurons in the visible layer,and the state variables of hidden neurons are calculated by taking the state value of the visible layer as the input value of the hidden layer according to the dynamic correlation of facial features.The recognition process is divided into two steps:training and forward propagation.The feature change rule is output.In the forward propagation process,the pixel point that conforms to the rule change is found,and the weight of the pixel point is solved.And as a loss function standard,the recognition weight of multiple positions on the face is used to constrain the recognition rate,and the dynamic recognition of facial partial occlusion expression is completed.Experimental data show that the proposed method can reduce image distortion and detail loss,improve image resolution,and achieve high recognition rate.It can complete efficient recognition for different local occlusion situations.

关 键 词：深度学习 表情动态识别 动态关联性 深度信念网络模型 隐藏层 

分 类 号：TP256[自动化与计算机技术—检测技术与自动化装置]