Enhancing Local Binary Patterns Distinctiveness for Face Representation

Abstract

The Local Binary pattern (LBP) is a well-known feature and has been widely used for human identification. However, the amount of information extracted is limited which reduces the LBP discriminative power. Recently, some enhancements have been proposed by adding preprocessing stages or considering more neighbor pixels to enrich the extracted feature. In this paper, we propose Uniformly-sampled Thresholds for LBP (UTLBP) operator that increases the richness of information derived from the LBP feature. It outperforms other features in various probe sets of the large CAS-PEAL database for face recognition. Moreover, we collected a database of 25 families to verify the superiority of the proposed feature in the family verification. Results show that using the UTLBP, the total error in face recognition and family verification is reduced up to 8% and 3% respectively comparing to the state of the art LBP. It improves the missing family member verification performance up to 3% where, contrary to expectation, increasing the LBP operator radius worsens the performance by 2%.