Authors: Elhocine Boutellaa; Farid Harizi; Messaoud Bengherabi; Samy Ait-Aoudia; Abdenour Hadid
Addresses: Centre de Développement des Technologies Avancées, BP 17, Baba Haasen 16081, Algeria ' Centre de Développement des Technologies Avancées, BP 17, Baba Haasen 16081, Algeria ' Centre de Développement des Technologies Avancées, BP 17, Baba Haasen 16081, Algeria ' Ecole Nationale Supèrieure d'Informatique, Oued-Smar 16270, Algeria ' University of Oulu, PL 8000, Oulu FI-90014, Finland
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.
Keywords: local binary patterns; LBPs; vector quantisation; a posteriori adaptation; VQMAP; face verification; histogram adaptation; biometrics; generic model adaptation; face recognition; face models.
International Journal of Biometrics, 2015 Vol.7 No.1, pp.31 - 44
Received: 08 Dec 2013
Accepted: 16 Dec 2014
Published online: 19 May 2015 *