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Conference Paper: Deep learning identity-preserving face space

TitleDeep learning identity-preserving face space
Authors
Keywordsface recognition
deep learning
Issue Date2013
Citation
Proceedings of the IEEE International Conference on Computer Vision, 2013, p. 113-120 How to Cite?
AbstractFace recognition with large pose and illumination variations is a challenging problem in computer vision. This paper addresses this challenge by proposing a new learning based face representation: the face identity-preserving (FIP) features. Unlike conventional face descriptors, the FIP features can significantly reduce intra-identity variances, while maintaining discriminative ness between identities. Moreover, the FIP features extracted from an image under any pose and illumination can be used to reconstruct its face image in the canonical view. This property makes it possible to improve the performance of traditional descriptors, such as LBP [2] and Gabor [31], which can be extracted from our reconstructed images in the canonical view to eliminate variations. In order to learn the FIP features, we carefully design a deep network that combines the feature extraction layers and the reconstruction layer. The former encodes a face image into the FIP features, while the latter transforms them to an image in the canonical view. Extensive experiments on the large MultiPIE face database [7] demonstrate that it significantly outperforms the state-of-the-art face recognition methods. © 2013 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/273663

 

DC FieldValueLanguage
dc.contributor.authorZhu, Zhenyao-
dc.contributor.authorLuo, Ping-
dc.contributor.authorWang, Xiaogang-
dc.contributor.authorTang, Xiaoou-
dc.date.accessioned2019-08-12T09:56:18Z-
dc.date.available2019-08-12T09:56:18Z-
dc.date.issued2013-
dc.identifier.citationProceedings of the IEEE International Conference on Computer Vision, 2013, p. 113-120-
dc.identifier.urihttp://hdl.handle.net/10722/273663-
dc.description.abstractFace recognition with large pose and illumination variations is a challenging problem in computer vision. This paper addresses this challenge by proposing a new learning based face representation: the face identity-preserving (FIP) features. Unlike conventional face descriptors, the FIP features can significantly reduce intra-identity variances, while maintaining discriminative ness between identities. Moreover, the FIP features extracted from an image under any pose and illumination can be used to reconstruct its face image in the canonical view. This property makes it possible to improve the performance of traditional descriptors, such as LBP [2] and Gabor [31], which can be extracted from our reconstructed images in the canonical view to eliminate variations. In order to learn the FIP features, we carefully design a deep network that combines the feature extraction layers and the reconstruction layer. The former encodes a face image into the FIP features, while the latter transforms them to an image in the canonical view. Extensive experiments on the large MultiPIE face database [7] demonstrate that it significantly outperforms the state-of-the-art face recognition methods. © 2013 IEEE.-
dc.languageeng-
dc.relation.ispartofProceedings of the IEEE International Conference on Computer Vision-
dc.subjectface recognition-
dc.subjectdeep learning-
dc.titleDeep learning identity-preserving face space-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICCV.2013.21-
dc.identifier.scopuseid_2-s2.0-84898819011-
dc.identifier.spage113-
dc.identifier.epage120-

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