Spatial histogram* of Local Binary Pattern (LBP) and Local Gabor Binary Pattern (LGBP) has been successfully applied to face recognition and achieved state-of-the-art performance. Both LBP and LGBP utilize traditional histogram matching method such as histogram intersection for face classification. In this paper, we propose a statistical extension for L(G)BP similarity computation by introducing Fisher Discriminant Analysis (FDA) of the L(G)BP spatial histogram "features". More than a simple application of FDA, we have constructed Ensemble of Piecewise FDA (EPFDA) classifiers, each of which is designed using one segment of the entire spatial histogram features. We show that this extension not only greatly reduces the feature dimension but also brings very impressive performance improvement. Especially, we have made a large step to recognizing all the faces in the standard FERET face database.