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: Conference contribution

1 引文 (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English
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
2018-April
ISBN(列印)9781538646588
DOIs
出版狀態Published - 2018 九月 10
事件2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
持續時間: 2018 四月 152018 四月 20

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
國家Canada
城市Calgary
期間18-04-1518-04-20

指紋

Face recognition
Data structures

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

引用此文

Chen, S. H., Lee, Y. S., Hsu, Y. S., Wu, C-H., & Wang, J. C. (2018). Locality-preserving complex-valued Gaussian process latent variable model for robust face recognition. 於 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (卷 2018-April, 頁 2696-2700). [8462680] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8462680
Chen, Sih Huei ; Lee, Yuan Shan ; Hsu, Yu Sheng ; Wu, Chung-Hsien ; Wang, Jia Ching. / Locality-preserving complex-valued Gaussian process latent variable model for robust face recognition. 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. 卷 2018-April Institute of Electrical and Electronics Engineers Inc., 2018. 頁 2696-2700
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abstract = "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.",
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Chen, SH, Lee, YS, Hsu, YS, Wu, C-H & Wang, JC 2018, Locality-preserving complex-valued Gaussian process latent variable model for robust face recognition. 於 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. 卷 2018-April, 8462680, Institute of Electrical and Electronics Engineers Inc., 頁 2696-2700, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 18-04-15. https://doi.org/10.1109/ICASSP.2018.8462680

Locality-preserving complex-valued Gaussian process latent variable model for robust face recognition. / Chen, Sih Huei; Lee, Yuan Shan; Hsu, Yu Sheng; Wu, Chung-Hsien; Wang, Jia Ching.

2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. 卷 2018-April Institute of Electrical and Electronics Engineers Inc., 2018. p. 2696-2700 8462680.

研究成果: Conference contribution

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AB - 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.

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Chen SH, Lee YS, Hsu YS, Wu C-H, Wang JC. Locality-preserving complex-valued Gaussian process latent variable model for robust face recognition. 於 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. 卷 2018-April. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2696-2700. 8462680 https://doi.org/10.1109/ICASSP.2018.8462680