Boosting local binary pattern (LBP)-based face recognition

This paper presents a novel approach for face recognition by boosting statistical local features based classifiers. The face image is scanned with a scalable sub-window from which the Local Binary Pattern (LBP) histograms [14] are obtained to describe the local features of a face image. The multi-class problem of face recognition is transformed into a two-class one by classifying every two face images as intra-personal or extra-personal ones [9]. The Chi square distance between corresponding Local Binary Pattern histograms of two face images is used as discriminative feature for intra/extra-personal classification. We use AdaBoost algorithm to learn a similarity of every face image pairs. The proposed method was tested on the FERET FA/FB image sets and yielded an exciting recognition rate of 97.9%.