An In-depth Examination of Local Binary Descriptors in Unconstrained Face Recognition
Automatic face recognition in unconstrained conditions is a difficult task which has recently attained increasing attention. In this domain, face verification methods have significantly improved since the release of the Labeled Faces in the Wild database, but the related problem of face identification, is still lacking considerations, which is partly because of the shortage of representative databases. Only recently, two new datasets called Remote Face and Point-and-Shoot Challenge were published providing appropriate benchmarks for the research community to investigate the problem of face recognition in challenging imaging conditions, in both, verification and identification modes. In this paper we provide an in-depth examination of three local binary description methods in unconstrained face recognition evaluating them on these two recently published datasets. In detail, we investigate three well established methods separately and fusing them at rank- and score-levels. We are using a well-defined evaluation protocol allowing a fair comparison of our results for future examinations.