一种深度自编码器面部表情识别新方法
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摘要
为解决面部表情特征维度高的问题,该文提出了一种基于深度学习自编码器的表情识别新方法,该方法利用深度自编码器在多层隐层上进行特征选择,能够在较低维度上表示高维度的面部特征.首先采用定向梯度直方图从面部表情的选定区域提取特征,然后在多个层面上使用深度自编码器,得到最优编码特征,降低特征维度,最后使用支持向量机模型对降维特征进行分类.实验表明,与其他现有特征选择和降维技术相比,该文方法提取的特征优于其他特征,并能够有效实现面部表情识别.
To solve the problem of high facial expression feature dimensions, a new method for facial expression recognition based on deep autoencoders has been presented in this paper. In this method, autoencoders is used to perform feature selection on multi-layer hidden layers, which can be able to represent high-dimensional facial features in lower dimensions. Firstly, the histograms of oriented gradients is used to extract features from selected regions of facial expressions. And then deep autoencoders is used on multiple levels to obtain the best coding feature, reducing the feature dimension. Finally, the support vector machine model is used to classify the dimensionality reduction features. Experiments show that the features extracted from the deep autoencoder outperformed when compared to other feature selection and dimension reduction techniques, and can effectively realize facial expression recognition.
To solve the problem of high facial expression feature dimensions, a new method for facial expression recognition based on deep autoencoders has been presented in this paper. In this method, autoencoders is used to perform feature selection on multi-layer hidden layers, which can be able to represent high-dimensional facial features in lower dimensions. Firstly, the histograms of oriented gradients is used to extract features from selected regions of facial expressions. And then deep autoencoders is used on multiple levels to obtain the best coding feature, reducing the feature dimension. Finally, the support vector machine model is used to classify the dimensionality reduction features. Experiments show that the features extracted from the deep autoencoder outperformed when compared to other feature selection and dimension reduction techniques, and can effectively realize facial expression recognition.
引文
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[14] 杨金鑫,杨辉华,李灵巧,等.结合卷积神经网络和超像素聚类的细胞图像分割方法 [J].计算机应用研究,2018,35(5):1569-1572,1577.
[2] 杨雨浓,房鼎益,王洪.一种基于混合深度置信模型的面部表情识别方法 [J].西南大学学报(自然科学版),2016,38(6):142-147.
[3] KHAN S A,HUSSAIN A,USMAN M.Reliable Facial Expression Recognition for Multi-Scale Images Using Weber Local Binary Image Based Cosine Transform Features [J].Multimedia Tools and Applications,2018,77(1):1133-1165.
[4] 何俊,何忠文,蔡建峰,等.一种新的多角度人脸表情识别方法 [J].计算机应用研究,2018,35(1):282-286.
[5] 杨勇,蔡舒博.一种基于两步降维和并行特征融合的表情识别方法 [J].重庆邮电大学学报(自然科学版),2015,27(3):377-385.
[6] SIDDIQI M H,ALI R,KHAN A M,et al.Human Facial Expression Recognition Using Stepwise Linear Discriminant Analysis and Hidden Conditional Random Fields [J].IEEE Transactions on Image Processing,2015,24(4):1386-1398.
[7] JUNG H,LEE S,YIM J,et al.Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition [C].Santiago:IEEE International Conference on Computer Vision (ICCV),2015.
[8] LIU Y P,LI Y B,MA X,et al.Facial Expression Recognition with Fusion Features Extracted from Salient Facial Areas [J].Sensors,2017,17(4):712-728.
[9] ZENG N Y,ZHANG H,SONG B Y,et al.Facial Expression Recognition Via Learning Deep Sparse Autoencoders [J].Neurocomputing,2018,273:643-649.
[10] XING Y X,LUO W.Facial Expression Recognition Using Local Gabor Features and Adaboost Classifiers [C].Shanghai:2016 International Conference on Progress in Informatics and Computing (PIC),2016.
[11] COSSETIN M J,NIEVOLA J C,KOERICH A L.Facial Expression Recognition Using a Pairwise Feature Selection and Classification Approach [C].Vancouver:2016 International Joint Conference on Neural Networks (IJCNN),2016.
[12] KAR N B,BABU K S,JENA S K.Face Expression Recognition Using Histograms of Oriented Gradients with Reduced Features[M]//Advances in Intelligent Systems and Computing.Singapore:Springer Singapore,2016.
[13] LIU L,FIEGUTH P,GUO Y L,et al.Local Binary Features for Texture Classification:Taxonomy and Experimental Study [J].Pattern Recognition,2017,62:135-160.
[14] 杨金鑫,杨辉华,李灵巧,等.结合卷积神经网络和超像素聚类的细胞图像分割方法 [J].计算机应用研究,2018,35(5):1569-1572,1577.