An Effective Unconstrained Correlation Filter and Its Kernelization for Face Recognition

TL;DR

This paper introduces an unconstrained correlation filter, UOOTF, and its kernelized version, which enhance face recognition robustness by removing origin constraints and handling non-linear class separations.

Contribution

The paper proposes a novel unconstrained correlation filter and its kernel extension, improving face recognition performance over traditional methods.

Findings

UOOTF outperforms conventional correlation filters in face recognition.

Kernel UOOTF effectively handles non-linear class distributions.

Experimental results confirm the robustness and effectiveness of the proposed methods.

Abstract

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.