Face recognition using color local binary pattern from mutually independent color channels

TL;DR

This paper introduces a face recognition system that leverages color local binary patterns from independent color channels, enhanced by wavelet and SVD techniques, achieving high accuracy across multiple face databases.

Contribution

The paper presents a novel face recognition approach combining color LBP with DWT and SVD for illumination enhancement and uses Kullback-Leibler Distance for improved recognition accuracy.

Findings

Achieved 99.78% recognition rate on FERET database.

Outperformed conventional gray scale LBP and PCA methods.

Demonstrated robustness to illumination and rotation.

Abstract

In this paper, a high performance face recognition system based on local binary pattern (LBP) using the probability distribution functions (PDF) of pixels in different mutually independent color channels which are robust to frontal homogenous illumination and planer rotation is proposed. The illumination of faces is enhanced by using the state-of-the-art technique which is using discrete wavelet transform (DWT) and singular value decomposition (SVD). After equalization, face images are segmented by use of local Successive Mean Quantization Transform (SMQT) followed by skin color based face detection system. Kullback-Leibler Distance (KLD) between the concatenated PDFs of a given face obtained by LBP and the concatenated PDFs of each face in the database is used as a metric in the recognition process. Various decision fusion techniques have been used in order to improve the recognition rate. The proposed system has been tested on the FERET, HP, and Bosphorus face databases. The proposed system is compared with conventional and thestate-of-the-art techniques. The recognition rates obtained using FVF approach for FERET database is 99.78% compared with 79.60% and 68.80% for conventional gray scale LBP and Principle Component Analysis (PCA) based face recognition techniques respectively.