國立虎尾科技大學 |

Representation Learning and Image Synthesis for Deep Face Recognition.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Representation Learning and Image Synthesis for Deep Face Recognition./
作者:	Yin, Xi.
面頁冊數:	1 online resource (124 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780438289192

Representation Learning and Image Synthesis for Deep Face Recognition.
Yin, Xi.

Representation Learning and Image Synthesis for Deep Face Recognition. - 1 online resource (124 pages)

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (Ph.D.)--Michigan State University, 2018.

Includes bibliographical references

Face recognition has been advanced a lot in recent years thanks to the development of deep neural networks. The large intra-class variations in pose, illumination, and expression are the long-standing challenges. Learning a discriminative representation that is robust to these variations is the key. In the scenarios of profile pose or long-tail training data, image or feature-level data augmentation is needed. This dissertation presents three different methods to solve these problems. First, we explore a multi-task Convolutional Neural Network (CNN) that aims to leverage side tasks to improve representation learning. A pose-directed multi-task CNN is introduced to better handle pose variation. The proposed framework is effective in pose-invariant face recognition. Second, we propose a Face Frontalization-Generative Adversarial Network (FF-GAN) that can generate a frontal face even from an input image with extreme profile pose. FF-GAN handles pose variation from the perspective of image-level data augmentation. Multiple loss functions are proposed to achieve large-pose face frontalization. The proposed approach is evaluated on various tasks including face reconstruction, landmark detection, face frontalization, and face recognition. Third, a feature transfer learning method is presented to solve the problem of insufficient intra-class variation via feature-level data augmentation. A Gaussian prior is assumed across all the regular classes and the variance are transferred from regular classes to long-tail classes. Further, an alternating training regimen is proposed to simultaneously achieve less biased decision boundaries and more discriminative representations. Extensive experiments have demonstrated the effectiveness of the proposed feature transfer framework.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438289192Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Representation Learning and Image Synthesis for Deep Face Recognition.
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502 $a Thesis (Ph.D.)--Michigan State University, 2018.
504 $a Includes bibliographical references
520 $a Face recognition has been advanced a lot in recent years thanks to the development of deep neural networks. The large intra-class variations in pose, illumination, and expression are the long-standing challenges. Learning a discriminative representation that is robust to these variations is the key. In the scenarios of profile pose or long-tail training data, image or feature-level data augmentation is needed. This dissertation presents three different methods to solve these problems. First, we explore a multi-task Convolutional Neural Network (CNN) that aims to leverage side tasks to improve representation learning. A pose-directed multi-task CNN is introduced to better handle pose variation. The proposed framework is effective in pose-invariant face recognition. Second, we propose a Face Frontalization-Generative Adversarial Network (FF-GAN) that can generate a frontal face even from an input image with extreme profile pose. FF-GAN handles pose variation from the perspective of image-level data augmentation. Multiple loss functions are proposed to achieve large-pose face frontalization. The proposed approach is evaluated on various tasks including face reconstruction, landmark detection, face frontalization, and face recognition. Third, a feature transfer learning method is presented to solve the problem of insufficient intra-class variation via feature-level data augmentation. A Gaussian prior is assumed across all the regular classes and the variance are transferred from regular classes to long-tail classes. Further, an alternating training regimen is proposed to simultaneously achieve less biased decision boundaries and more discriminative representations. Extensive experiments have demonstrated the effectiveness of the proposed feature transfer framework.
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