Subjective Performance of Texture Based Algorithm for Face Verification: The Role of Databases

This paper address new face verification scheme based on Log-Gabor filter (texture based) and Gaussian Mixture Model. The proposed method consists of three parts. The first part is a Log-Gabor filtering on facial image. The second part is to model the Log-Gabor filter response using Gaussian Mixture Model to obtain more than one set of features. The third part is transforming the set of features using subspace methods such as principal component analysis (PCA) and independent component analysis (ICA). Thus, in this paper two methods namely Log-Gabor mixture model (LGMM) based on PCA and Log-Gabor mixture model based (LGMM) on ICA is proposed. Proposed methods are evaluated for its performance by conducting series of experiments on three image databases: ORL, AR, YALE face database. The nature of type & size of databases chosen on the performance of these algorithms are also studied. The experimental results indicate the efficacy of the proposed methods and varied nature of results based on these algorithms.