Two-stage tensor locality-preserving projection face recognition

Ying Liu; Dimitris A. Pados; Chia Hung Yeh

doi:10.1109/BigMM.2016.31

Two-stage tensor locality-preserving projection face recognition

Ying Liu, Dimitris A. Pados^*, Chia Hung Yeh

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Locality-preserving projection (LPP) is an efficient dimensionality reduction approach that preserves local relationships within data sets and uncovers essential manifold structures. In this paper, we develop a two-stage tensor locality-preserving projection for face recognition, in which first-stage tensor LPP is performed in the original tensor space of face images and second stage tensor LPP is performed in the reduced-dimension tensor subspace of the first-stage projection. For classification, we seek a non-negative sparse representation in the final low-dimensional tensor subspace and determine the class of an unknown face image by minimum sparse representation error. Experimental studies demonstrate that our proposed two-stage tensor LPP scheme along with the non-negative sparse representation classifier effectively exploits the locality structure of face images and outperforms existing state-of-the-art face recognition schemes.

Original language	English
Title of host publication	Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	214-218
Number of pages	5
ISBN (Electronic)	9781509021789
DOIs	https://doi.org/10.1109/BigMM.2016.31
Publication status	Published - 2016 Aug 16
Externally published	Yes
Event	2nd IEEE International Conference on Multimedia Big Data, BigMM 2016 - Taipei, Taiwan Duration: 2016 Apr 20 → 2016 Apr 22

Publication series

Name	Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016

Conference

Conference	2nd IEEE International Conference on Multimedia Big Data, BigMM 2016
Country/Territory	Taiwan
City	Taipei
Period	2016/04/20 → 2016/04/22

Keywords

Classification
Face recognition
Locality preserving projection
Non-negative sparse representation
Tensor subspace

ASJC Scopus subject areas

Signal Processing
Information Systems
Media Technology

Access to Document

10.1109/BigMM.2016.31

Cite this

Liu, Y., Pados, D. A., & Yeh, C. H. (2016). Two-stage tensor locality-preserving projection face recognition. In Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016 (pp. 214-218). Article 7545024 (Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigMM.2016.31

Two-stage tensor locality-preserving projection face recognition. / Liu, Ying; Pados, Dimitris A.; Yeh, Chia Hung.
Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 214-218 7545024 (Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Liu, Y, Pados, DA & Yeh, CH 2016, Two-stage tensor locality-preserving projection face recognition. in Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016., 7545024, Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 214-218, 2nd IEEE International Conference on Multimedia Big Data, BigMM 2016, Taipei, Taiwan, 2016/04/20. https://doi.org/10.1109/BigMM.2016.31

Liu Y, Pados DA, Yeh CH. Two-stage tensor locality-preserving projection face recognition. In Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 214-218. 7545024. (Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016). doi: 10.1109/BigMM.2016.31

Liu, Ying ; Pados, Dimitris A. ; Yeh, Chia Hung. / Two-stage tensor locality-preserving projection face recognition. Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 214-218 (Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016).

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title = "Two-stage tensor locality-preserving projection face recognition",

abstract = "Locality-preserving projection (LPP) is an efficient dimensionality reduction approach that preserves local relationships within data sets and uncovers essential manifold structures. In this paper, we develop a two-stage tensor locality-preserving projection for face recognition, in which first-stage tensor LPP is performed in the original tensor space of face images and second stage tensor LPP is performed in the reduced-dimension tensor subspace of the first-stage projection. For classification, we seek a non-negative sparse representation in the final low-dimensional tensor subspace and determine the class of an unknown face image by minimum sparse representation error. Experimental studies demonstrate that our proposed two-stage tensor LPP scheme along with the non-negative sparse representation classifier effectively exploits the locality structure of face images and outperforms existing state-of-the-art face recognition schemes.",

keywords = "Classification, Face recognition, Locality preserving projection, Non-negative sparse representation, Tensor subspace",

author = "Ying Liu and Pados, {Dimitris A.} and Yeh, {Chia Hung}",

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N2 - Locality-preserving projection (LPP) is an efficient dimensionality reduction approach that preserves local relationships within data sets and uncovers essential manifold structures. In this paper, we develop a two-stage tensor locality-preserving projection for face recognition, in which first-stage tensor LPP is performed in the original tensor space of face images and second stage tensor LPP is performed in the reduced-dimension tensor subspace of the first-stage projection. For classification, we seek a non-negative sparse representation in the final low-dimensional tensor subspace and determine the class of an unknown face image by minimum sparse representation error. Experimental studies demonstrate that our proposed two-stage tensor LPP scheme along with the non-negative sparse representation classifier effectively exploits the locality structure of face images and outperforms existing state-of-the-art face recognition schemes.

AB - Locality-preserving projection (LPP) is an efficient dimensionality reduction approach that preserves local relationships within data sets and uncovers essential manifold structures. In this paper, we develop a two-stage tensor locality-preserving projection for face recognition, in which first-stage tensor LPP is performed in the original tensor space of face images and second stage tensor LPP is performed in the reduced-dimension tensor subspace of the first-stage projection. For classification, we seek a non-negative sparse representation in the final low-dimensional tensor subspace and determine the class of an unknown face image by minimum sparse representation error. Experimental studies demonstrate that our proposed two-stage tensor LPP scheme along with the non-negative sparse representation classifier effectively exploits the locality structure of face images and outperforms existing state-of-the-art face recognition schemes.

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KW - Non-negative sparse representation

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Two-stage tensor locality-preserving projection face recognition

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

Access to Document

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