Palmprint recognition based on the representation in the feature space

In this paper, we propose a palmprint recognition method based on the representation in the feature space. The proposed method seeks to represent the test sample as a linear combination of all the training samples in the feature space and then exploits the obtained linear combination to perform palmprint recognition. We can implement the mapping from the original space to the feature space by using the kernel functions such as radial basis function (RBF). In this method, the selection of the parameter of the kernel function is important. We propose an automatic algorithm for selecting the parameter. The basic idea of the algorithm is to optimize the feature space such that the samples from the same class are well clustered while the samples from different classes are pushed far away. The proposed criterion measures the goodness of a feature space, and the optimal kernel parameter is obtained by minimizing this criterion. Experimental results on multispectral palmprint database show that the proposed method is more effective than 2DPCA, 2DLDA, AANNC, CRC_RLS, nearest neighbor method (NN) and competitive coding method in terms of the correct recognition rate.