Face verification using local binary patterns and generic model adaptation

Abstract

The popular local binary patterns LBP have been highly successful in representing and recognising faces. However, the original LBP-based face recognition method has some problems that need to be addressed. In this work, we propose two approaches to address the histogram representation drawbacks in the LBP-based face verification system. The first approach employs vector quantisation maximum a posteriori adaptation VQMAP model, where a generic face model is obtained by vector quantisation and the user models are inferred using maximum a posteriori adaptation. The second approach proposes an enhanced LBP histogram representation by adapting a generic face histogram to each user. Moreover, the two proposed approaches are further fused to enhance the verification performance. We evaluate our proposed approaches on two publicly available databases, namely BANCA and XM2VTS, and compare the results against the original LBP approach and its variants, demonstrating very promising results.