New Fuzzy LBP Features for Face Recognition

TL;DR

This paper introduces a novel face recognition method combining Local Binary Pattern features with fuzzy membership functions to improve discriminative power and computational efficiency, validated on standard datasets.

Contribution

The paper proposes a simple, effective approach integrating fuzzy logic with LBP for face recognition, addressing limitations of traditional LBP methods.

Findings

High recognition accuracy on ORL and Sheffield databases

Effective elimination of traditional LBP limitations

Robust performance demonstrated through K-fold and ROC analysis

Abstract

There are many Local texture features each very in way they implement and each of the Algorithm trying improve the performance. An attempt is made in this paper to represent a theoretically very simple and computationally effective approach for face recognition. In our implementation the face image is divided into 3x3 sub-regions from which the features are extracted using the Local Binary Pattern (LBP) over a window, fuzzy membership function and at the central pixel. The LBP features possess the texture discriminative property and their computational cost is very low. By utilising the information from LBP, membership function, and central pixel, the limitations of traditional LBP is eliminated. The bench mark database like ORL and Sheffield Databases are used for the evaluation of proposed features with SVM classifier. For the proposed approach K-fold and ROC curves are obtained and results are compared.