GridLDA of Gabor wavelet features for palmprint identification

Abstract

In this paper, we propose a novel palmprint recognition algorithm based on using GridLDA for Gabor wavelet features. Our proposed method includes two main steps for palmprint feature extraction: (1) Local invariant features are extracted by computing the Gabor wavelet Engergy of the original images that handles the palm structure and the variations of illumination. (2) An improved two-dimensional Linear Discriminant Analysis, called GridLDA, is then applied to further remove redundant information and form a discriminant representation more suitable for palmprint recognition. The experimental results for the identification on public database of Hong Kong Polytechnic University (PolyU) demonstrate the effectiveness of the proposed method.