The use of the symmetric finite difference in the local binary pattern (symmetric LBP)

TL;DR

This paper introduces a symmetric LBP method that reduces feature dimensionality by using four directions instead of eight, aiming to improve face detection and expression recognition.

Contribution

It proposes a novel symmetric LBP approach that simplifies the standard method by employing fewer directions, reducing features from 256 to 16.

Findings

Significant reduction in LBP features from 256 to 16.

Enhanced focus on the importance of directional choices in LBP.

Potential improvements in face detection and expression recognition accuracy.

Abstract

The paper provides a mathematical view to the binary numbers presented in the Local Binary Pattern (LBP) feature extraction process. Symmetric finite difference is often applied in numerical analysis to enhance the accuracy of approximations. Then, the paper investigates utilization of the symmetric finite difference in the LBP formulation for face detection and facial expression recognition. It introduces a novel approach that extends the standard LBP, which typically employs eight directional derivatives, to incorporate only four directional derivatives. This approach is named symmetric LBP. The number of LBP features is reduced to 16 from 256 by the use of the symmetric LBP. The study underscores the significance of the number of directions considered in the new approach. Consequently, the results obtained emphasize the importance of the research topic.