An effective unconstrained correlation filter and its kernelization for face recognition

• An unconstrained correlation filter UOOTF is proposed for robust face recognition.
• UOOTF overcomes the problem of UOTF by emphasizing the origin correlation outputs.
• UOOTF improves the overall performance by removing the hard constraints.
• We further develop a non-linear extension of UOOTF based on the kernel technique.
• Experiments show UOOTF and its kernelization perform favorably on 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.