Face Verification using Gabor filtering and adapted Gaussian Mixture Models
Laurent El Shafey, Roy Wallace, Sébastien Marcel · 2012
Abstract: The search for robust features for face recognition in uncontrolled environ-ments is an important topic of research. In particular, there is a high interest in Gabor-based features which have invariance properties to simple geometrical transformations. In this paper, we first reinterpret Gabor filtering as a frequency decomposition into bands, and analyze the influence of each band separately for face recognition. Then, a new face verification scheme is proposed, combining the strengths of Gabor filtering with Gaussian Mixture Model (GMM) modelling. Finally, this new system is evalu-ated on the BANCA and MOBIO databases with respect to well known face recogni-tion algorithms. The proposed system demonstrates up to 52 % relative improvement in verification error rate compared to a standard GMM approach, and outperforms the state-of-the-art Local Gabor Binary Pattern Histogram Sequence (LGBPHS) technique for several face verification protocols on two different databases. 1