  In this paper, an effective unconstrained correlation filter called Uncon-
strained 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 ex- tension of
UOOTF allows for higher flexibility of the decision boundary due to a wider
range of non-linearity properties. Experimental results demon- strate the
effectiveness of the proposed unconstrained correlation filter and its
kernelization in the task of face recognition.
