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Regression-Based Face Pose Estimation with Deep Multi-modal Feature Loss
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作者 Yanqiu Wu Chaoqun Hong +1 位作者 Liang Chen Zhiqiang Zeng 《国际计算机前沿大会会议论文集》 2020年第1期534-549,共16页
Image-based face pose estimation tries to estimate the facial direction with 2D images.It provides important information for many face recognition applications.However,it is a difficult task due to complex conditions ... Image-based face pose estimation tries to estimate the facial direction with 2D images.It provides important information for many face recognition applications.However,it is a difficult task due to complex conditions and appearances.Deep learning method used in this field has the disadvantage of ignoring the natural structures of human faces.To solve this problem,a framework is proposed in this paper to estimate face poses with regression,which is based on deep learning and multi-modal feature loss(M2FL).Different from current loss functions using only a single type of features,the descriptive power was improved by combining multiple image features.To achieve it,hypergraph-based manifold regularization was applied.In this way,the loss of face pose estimation was reduced.Experimental results on commonly-used benchmark datasets demonstrate the performance of M2FL. 展开更多
关键词 face pose estimation Deep learning Multi-modal features
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3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks 被引量:1
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作者 Khalil Khan Jehad Ali +6 位作者 Kashif Ahmad Asma Gul Ghulam Sarwar Sahib Khan Qui Thanh Hoai Ta Tae-Sun Chung Muhammad Attique 《Computers, Materials & Continua》 SCIE EI 2021年第2期1757-1770,共14页
Face image analysis is one among several important cues in computer vision.Over the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis ... Face image analysis is one among several important cues in computer vision.Over the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis tasks.Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation.In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model.We have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and back.We extract features from gray scale images by using DCNNs.We train a classifier using the extracted features.We use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic class.We use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation phase.We assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and ICT-3DHP,obtaining superior results as compared to previous results. 展开更多
关键词 face image analysis face parsing face pose estimation
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