Quadratic projection based feature extraction with its application to biometric recognition

Yan Yan, Hanzi Wang, Si Chen, Xiaochun Cao, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)


We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the conventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, based on the Lagrange duality theory, to extract nonlinear features. To evaluate the feasibility and effectiveness of the proposed framework, we conduct extensive experiments on biometric recognition. Experimental results on three representative biometric recognition tasks, including face, palmprint, and ear recognition, demonstrate the superiority of the DualQML-based feature extraction algorithm compared to the current state-of-the-art algorithms.
Original languageEnglish
Pages (from-to)40-49
Number of pages10
JournalPattern Recognition
Publication statusPublished - 1 Aug 2016


  • Biometric recognition
  • Feature extraction
  • Lagrange duality
  • Quadratic projection
  • Semidefinite programming

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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