Palmprint recognition using Gabor feature-based (2D)2PCA

In this paper, we propose a novel approach of Gabor feature-based (2D)2PCA (GB(2D)2PCA) for palmprint recognition. Three main steps are involved in the proposed GB(2D)2PCA: (i) Gabor features of different scales and orientations are extracted by the convolution of Gabor filter bank and the original gray images; (ii) (2D)2PCA is then applied for dimensionality reduction of the feature space in both row and column directions; and (iii) Euclidean distance and the nearest neighbor classifier are finally used for classification. The method is not only robust to illumination and rotation, but also efficient in feature matching. Experimental results demonstrate the effectiveness of our proposed GB(2D)2PCA in both accuracy and speed.