基于深度网络的人脸区域分割方法
|
摘要
语义分割是近年来比较热的一个主题,而其中对二维人脸图片的区域分割技术的研究,对机器人应用,人脸头部姿势预测,三维人脸识别,分割,动画等方面有重要促进意义。由于目前的人脸区域分割算法在精度上存在一定不足,提出了基于深度网络的人脸区域分割方法,并进行了实验。实验结果表明该算法相较于以前的一些方法精度更高,鲁棒性好,有实际应用意义。
Semantic segmentation is a popular theme in recent years. The two-dimensional face image segmentation technology is important for the field of robot industry, the head pose prediction, three-dimensional face recognition, face segmentation, facial animation and other fields. Because of the shortage of the existing segmentation algorithm, it proposes a face region segmentation method based on deep network. The experimental results show that the algorithm is of high precision and good robustness, which has practical application significance.
Semantic segmentation is a popular theme in recent years. The two-dimensional face image segmentation technology is important for the field of robot industry, the head pose prediction, three-dimensional face recognition, face segmentation, facial animation and other fields. Because of the shortage of the existing segmentation algorithm, it proposes a face region segmentation method based on deep network. The experimental results show that the algorithm is of high precision and good robustness, which has practical application significance.
引文
[1]Saito S,Li T,Li H.Real-time facial segmentation and performance capture[C]//Computer Vision-ECCV,2016:244-261.
[2]黄建,李文书,高玉娟.人脸表情识别研究进展[J].计算机科学,2016(S2).
[3]Khan K,Mauro M,Migliorati P,et al.Head pose estimation through multi-class face segmentation[C]//2017 IEEEInternational Conference on Multimedia and Expo,2017:175-180.
[4]李建锋,梅楚璇,廖胜辉.一种自动3D面部区域分割方法[J].中南大学学报(自然科学版),2017,48(7):1797-1802.
[5]Khan K,Mauro M,Leonardi R.Multi-class semantic segmentation of faces[C]//2015 IEEE International Conference on Image Processing,2015:827-831.
[6]刘祥楼,邓艳茹,高丙坤.基于语义的人眼特征快速自动提取方法[J].科学技术与工程,2012(12):2849-2851.
[7]Kae A,Sohn K,Lee H,et al.Augmenting CRFs with Boltzmann machine shape priors for image labeling[C]//Computer Vision and Pattern Recognition,2013:2019-2026.
[8]Wu X.Fully convolutional networks for semantic segmentation[J].Computer Science,2015.
[9]Noh H,Hong S,Han B.Learning deconvolution network for semantic segmentation[C]//Proceedings of the IEEE International Conference on Computer Vision,2015:1520-1528.
[10]Badrinarayanan V,Kendall A,Cipolla R.SegNet:a deep convolutional encoder-decoder architecture for scene segmentation[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2017,39(12):2481-2495.
[11]Chen L C,Papandreou G,Kokkinos I,et al.DeepLab:semantic image segmentation with deep convolutional nets,atrous convolution,and fully connected CRFs[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2018,40(4):834-848.
[12]Paysan P,Knothe R,Amberg B,et al.A 3D face model for pose and illumination invariant face recognition[C]//2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance,2009:296-301.
[13]Jackson A S,Valstar M,Tzimiropoulos G.A CNN cascade for landmark guided semantic part segmentation[C]//European Conference on Computer Vision,2016:143-155.
[14]Cao C,Weng Y,Zhou S,et al.Face Warehouse:a 3D facial expression database for visual computing[J].IEEE Transactions on Visualization&Computer Graphics,2014,20(3):413-425.
[2]黄建,李文书,高玉娟.人脸表情识别研究进展[J].计算机科学,2016(S2).
[3]Khan K,Mauro M,Migliorati P,et al.Head pose estimation through multi-class face segmentation[C]//2017 IEEEInternational Conference on Multimedia and Expo,2017:175-180.
[4]李建锋,梅楚璇,廖胜辉.一种自动3D面部区域分割方法[J].中南大学学报(自然科学版),2017,48(7):1797-1802.
[5]Khan K,Mauro M,Leonardi R.Multi-class semantic segmentation of faces[C]//2015 IEEE International Conference on Image Processing,2015:827-831.
[6]刘祥楼,邓艳茹,高丙坤.基于语义的人眼特征快速自动提取方法[J].科学技术与工程,2012(12):2849-2851.
[7]Kae A,Sohn K,Lee H,et al.Augmenting CRFs with Boltzmann machine shape priors for image labeling[C]//Computer Vision and Pattern Recognition,2013:2019-2026.
[8]Wu X.Fully convolutional networks for semantic segmentation[J].Computer Science,2015.
[9]Noh H,Hong S,Han B.Learning deconvolution network for semantic segmentation[C]//Proceedings of the IEEE International Conference on Computer Vision,2015:1520-1528.
[10]Badrinarayanan V,Kendall A,Cipolla R.SegNet:a deep convolutional encoder-decoder architecture for scene segmentation[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2017,39(12):2481-2495.
[11]Chen L C,Papandreou G,Kokkinos I,et al.DeepLab:semantic image segmentation with deep convolutional nets,atrous convolution,and fully connected CRFs[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2018,40(4):834-848.
[12]Paysan P,Knothe R,Amberg B,et al.A 3D face model for pose and illumination invariant face recognition[C]//2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance,2009:296-301.
[13]Jackson A S,Valstar M,Tzimiropoulos G.A CNN cascade for landmark guided semantic part segmentation[C]//European Conference on Computer Vision,2016:143-155.
[14]Cao C,Weng Y,Zhou S,et al.Face Warehouse:a 3D facial expression database for visual computing[J].IEEE Transactions on Visualization&Computer Graphics,2014,20(3):413-425.