关键词: 非负稀疏编码, 局部特征提取, 掌纹识别, 径向基概率神经网络分类器

Abstract: To more effectively extract localized features of images, on the basis of the traditional Non-negative Sparse Coding (Hoyer NNSC) algorithm, a novel localized NNSC (LNNSC) algorithm with sparse constraint was proposed. This algorithm considered the sparse measure constraint of feature basis vectors and the maximized representativeness of features, and could obtain the strengthened localized image features. At the same time, this algorithm utilized the Laplace density model as the feature coefficients sparse punitive function to ensure an image's sparse structure. Furthermore, on the basis of feature extraction, by utilizing the Radial Basis Probabilistic Neural Networks (RBPNN), the palmprint recognition task could be implemented automatically. Compared with the palmprint recognition methods of Non-negative Matrix Factorization (NMF), Local NMF (LNMF) and Hoyer-NNSC, simulation results show that our method proposed here displays feasibility and practicality in palmprint recognition.

Key words: non-negative sparse coding, localized feature extraction, palmprint recognition, radial basis probabilistic neural network classifier