Hand Vein Recognition via Discriminative Convolutional Sparse Coding

Personal identification based on vein pattern is one of the latest biometric approaches that have attracted lots of attention. Besides, Convolutional Sparse Coding (CSC) is a popular model in the signal and image processing communities, resolving some limitations of the traditional patch-based sparse representations. As most existing CSC algorithms are suited for image restoration, we present a novel discriminative model based on CSC for dorsal hand vein recognition in this paper. The proposed method, discriminative local block coordinate descent (D-LoBCoD), is based on extending the LoBCoD algorithm by incorporating the classification error into the objective function that considers the performance of a linear classifier and the representational power of the filters simultaneously. Thus, for training, in each iteration, after updating the sparse coefficients and convolutional filters, we minimize the classification error by updating the classifier’s parameters according to the label information. Finally, after training, the label of the query image will be determined by the trained classifier. One thousand two hundred dorsal hand vein images taken from 100 individuals are used to verify the validity of the proposed methods. The experimental results show that our method outperforms other competing methods. Further, we demonstrate that our proposed method is less dependent on the number of training samples because of capturing more representative information from the corresponding images.