基于局部非负稀疏编码的掌纹识别方法

为了更有效地提取出图像的局部特征，在传统的非负稀疏编码(Hoyer-NNSC)算法的基础上，提出了一种新的具有稀疏度约束的局部NNSC (LNNSC)算法。该算法考虑了特征基向量的稀疏度约束和特征的最大化代表性，能够得到强化的图像局部特征；同时利用拉普拉斯密度模型作为特征系数的稀疏惩罚函数，保证了图像结构的稀疏性。在特征提取的基础上，进一步利用径向基概率神经网络(RBPNN)分类器，实现了掌纹的自动识别。仿真实验结果表明，与基于非负矩阵分解(NMF)、局部非负矩阵分解(LNMF)和Hoyer-NNSC的掌纹识别方法相比，该算法在掌纹识别研究中有较高的可行性和实用性。

Palmprint recognition method based on localized non-negative sparse coding

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.