DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification

作者: Shu Zhang , Ran He , Zhenan Sun , Tieniu Tan

DOI: 10.1109/TIFS.2017.2763119

关键词:

摘要: MeshFace photos have been widely used in many Chinese business organizations to protect ID face from being misused. The occlusions incurred by random meshes severely degenerate the performance of verification systems, which raises problem between and daily photos. Previous methods cast this as a typical low-level vision problem, i.e., blind inpainting. They recover perceptually pleasing clear MeshFaces enforcing pixel level similarity recovered images ground-truth then perform on them. Essentially, is conducted compact feature space rather than image space. Therefore, paper argues that jointly offer key improve performance. Based insight, we novel oriented inpainting framework. Specifically, implement establishing DeMeshNet, consists three parts. first part addresses implicitly exploiting extra supervision occlusion position enforce similarity. second explicitly enforces space, can explore informative produce better results for verification. last copes with alignment within net via customized spatial transformer module when extracting deep facial features. All parts are implemented an end-to-end network facilitates efficient optimization. Extensive experiments two data sets demonstrate effectiveness proposed DeMeshNet well insight paper.

参考文章(57)
Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution european conference on computer vision. pp. 184- 199 ,(2014) , 10.1007/978-3-319-10593-2_13
Dong Chen, Xudong Cao, Liwei Wang, Fang Wen, Jian Sun, Bayesian Face Revisited: A Joint Formulation Computer Vision – ECCV 2012. pp. 566- 579 ,(2012) , 10.1007/978-3-642-33712-3_41
Vijay Badrinarayanan, Roberto Cipolla, Ankur Handa, SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling computer vision and pattern recognition. ,(2015)
Koray Kavukcuoglu, Max Jaderberg, Karen Simonyan, Andrew Zisserman, Spatial transformer networks neural information processing systems. ,vol. 28, pp. 2017- 2025 ,(2015)
Qi Yin, Zhimin Cao, Erjin Zhou, Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not? arXiv: Computer Vision and Pattern Recognition. ,(2015)
Diederik P. Kingma, Jimmy Ba, Adam: A Method for Stochastic Optimization arXiv: Learning. ,(2014)
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification international conference on computer vision. pp. 1026- 1034 ,(2015) , 10.1109/ICCV.2015.123
Karen Simonyan, Andrew Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition computer vision and pattern recognition. ,(2014)
Hyeonwoo Noh, Seunghoon Hong, Bohyung Han, Learning Deconvolution Network for Semantic Segmentation international conference on computer vision. pp. 1520- 1528 ,(2015) , 10.1109/ICCV.2015.178
Marwan Mattar, Tamara Berg, Gary B. Huang, Eric Learned-Miller, Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition. ,(2008)