Two-stage tensor locality-preserving projection face recognition

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

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

3 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
主出版物標題Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016
發行者Institute of Electrical and Electronics Engineers Inc.
頁面214-218
頁數5
ISBN(電子)9781509021789
DOIs
出版狀態已發佈 - 2016 8月 16
對外發佈
事件2nd IEEE International Conference on Multimedia Big Data, BigMM 2016 - Taipei, 臺灣
持續時間: 2016 4月 202016 4月 22

出版系列

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

會議

會議2nd IEEE International Conference on Multimedia Big Data, BigMM 2016
國家/地區臺灣
城市Taipei
期間2016/04/202016/04/22

ASJC Scopus subject areas

  • 訊號處理
  • 資訊系統
  • 媒體技術

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