面向人脸表情分析的人脸图像正脸化方法  

Face image frontalization method for face expression analysis

作　　者：张学典[1] 陈钟军 秦晓飞[1] ZHANG Xuedian;CHEN Zhongjun;QIN Xiaofei(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《光学仪器》2023年第1期8-17,共10页Optical Instruments

基　　金：国家自然科学基金(92048205);国家重点研发计划(2021YFB2802300)。

摘　　要：在人脸表情分析过程中,头部姿态变化常会引起人脸信息的不对称,传统上仅对人脸图像进行裁剪和对齐的相关操作难以得到对姿态鲁棒的特征。为获取人脸结构化的特征,提出了一种人脸图像正脸化处理方法。该方法将检测到的人脸关键点映射到新的二维空间进行关键点的正脸化,将正脸化后的关键点还原到原始图像中作为新关键点,通过移动最小二乘法指导图像由原始关键点向新关键点变形,得到正脸化后的人脸图像。在公共的RAF-DB和ExpW人脸表情数据集上,采用上述处理方法对人脸图像进行预处理,并在VGG16和ResNet50深度学习网络中进行人脸表情分类任务的模型训练,用分类任务的准确率来评估文中正脸化方法对人脸表情分析的有效性。实验结果表明,该方法在人脸表情分析方面优于深度学习中传统的预处理方法,并且可以有效提高人脸的信息质量。In the process of face expression analysis,head pose changes often cause asymmetry in face information,and it is difficult to obtain features that are robust to pose by traditional operations related to cropping and aligning face images only.In order to obtain structured features of faces,a face image frontalization processing method is proposed in the paper.The method maps the detected face landmarks to a new two-dimensional space for frontalization of landmarks,then restores the frontalized landmarks to the original image as new landmarks,and guides the image deformation from the original landmarks to the new landmarks by moving least squares to obtain the frontalized face image.The face images are preprocessed on public RAF-DB and ExpW face expression datasets using the proposed processing method,models are trained in VGG16 and ResNet50 deep learning networks for face expression classification tasks.The effectiveness of the frontalization method in the paper for face expression analysis is evaluated by the accuracy of the classification tasks.The experimental results show that this proposed method outperforms traditional pre-processing methods of deep learning in face expression analysis and can effectively improve the quality of face information.

关 键 词：人脸表情分析 预处理 正脸化 表情分类 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]