Weighted Gabor Features in Unitary Space for Face Recognition

Abstract

Gabor filters based features, with their good properties of space-frequency localization and orientation selectivity, seem to be the most effective features for face recognition currently. In this paper, we propose a kind of weighted Gabor complex features which combining Gabor magnitude and phase features in unitary space. Its weights are determined according to recognition rates of magnitude and phase features. Meanwhile, subspace based algorithms, PCA and LDA, are generalized into unitary space, and a rarely used distance measure, unitary space cosine distance, is adopted for unitary subspace based recognition algorithms. Using the generalized subspace algorithms our proposed weighted Gabor complex features (WGCF) produce better recognition result than either Gabor magnitude or Gabor phase features. Experiments on FERET database show good results comparable to the best one reported in literature.