An effective unconstrained correlation filter and its kernelization for face recognition

In this paper, an effective unconstrained correlation filter called Unconstrained Optimal Origin Tradeoff Filter (UOOTF) is presented and applied to robust face recognition. Compared with the conventional correlation filters in Class-dependence Feature Analysis (CFA), UOOTF improves the overall performance for unseen patterns by removing the hard constraints on the origin correlation outputs during the filter design. To handle non-linearly separable distributions between different classes, we further develop a non-linear extension of UOOTF based on the kernel technique. The kernel extension of UOOTF allows for higher flexibility of the decision boundary due to a wider range of non-linearity properties. Experimental results demonstrate the effectiveness of the proposed unconstrained correlation filter and its kernelization in the task of face recognition.

• Publication year

  2013

• Venue

  Neurocomputing

• Publication date

  2013-11-01

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1016/j.neucom.2013.03.039arXiv 1603.07800
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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