Locality-preserving complex-valued Gaussian process latent variable model for robust face recognition

Sih Huei Chen, Yuan Shan Lee, Yu Sheng Hsu, Chung Hsien Wu, Jia Ching Wang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

1 引文 斯高帕斯(Scopus)

摘要

Learning a low-dimensional image representation yields effective and efficient face recognition. The use of such a representation helps to weaken the curse of dimensionality. However, the traditional facial representation method is not robust against partial occlusions or variations of expression. To solve this problem, this paper proposes a more reliable, complex-valued representation of facial image. The robust representation is based on the proposed locality-preserving complex-valued Gaussian process latent variable model (LP-CGPLVM). In the LP-CGPLVM, the Euler formula is utilized to transform original facial images into the complex domain. A proper complex GP is employed to model the mapping between the complex-valued high-dimensional data and the corresponding low-dimensional representation. Moreover, the locality-preserving constraint is taken into consideration to preserve the neighborhood data structure. The experimental results indicate that our proposed method is robust against partial occlusions and various facial expressions.

原文???core.languages.en_GB???
主出版物標題2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面2696-2700
頁數5
ISBN(列印)9781538646588
DOIs
出版狀態已出版 - 10 9月 2018
事件2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
持續時間: 15 4月 201820 4月 2018

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2018-April
ISSN(列印)1520-6149

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???event.eventtypes.event.conference???2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
國家/地區Canada
城市Calgary
期間15/04/1820/04/18

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