| 基于局部非负稀疏编码的掌纹识别方法 |
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| 引用本文： | 尚丽,苏品刚,杜吉祥. 基于局部非负稀疏编码的掌纹识别方法[J]. 计算机应用, 2011, 31(6): 1609-1612. DOI: 10.3724/SP.J.1087.2011.01609 |
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| 作者姓名： | 尚丽 苏品刚 杜吉祥 |
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| 作者单位： | 1. 苏州市职业大学 电子信息工程系, 江苏 苏州 2151042. 中国科学技术大学 信息科学技术学院, 合肥 2300263. 华侨大学 计算机科学与技术学院, 福建 泉州 362021 |
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| 基金项目： | 国家自然科学基金资助项目，江苏省自然科学基金资助项目，江苏省"青蓝工程"资助项目，苏州市职业大学创新团队基金资助项目 |
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| 摘 要： | 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。
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| 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 |
| 收稿时间： | 2010-10-14 |
| 修稿时间： | 2010-11-29 |
Palmprint recognition method based on localized non-negative sparse coding |
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| SHANG Li,SU Pin-gang,DU Ji-xiang. Palmprint recognition method based on localized non-negative sparse coding[J]. Journal of Computer Applications, 2011, 31(6): 1609-1612. DOI: 10.3724/SP.J.1087.2011.01609 |
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| Authors: | SHANG Li SU Pin-gang DU Ji-xiang |
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| Affiliation: | 1. Department of Electronic Information Engineering, Suzhou Vocational University, Suzhou Jiangsu 215104, China2. College of Computer Science and Technology, Huaqiao University, Quanzhou Fujian 362021, China3. College of Information and Technology, University of Science and Technology of China, Hefei Anhui 230026, China |
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| 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. |
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| Keywords: | non-negative sparse coding localized feature extraction palmprint recognition radial basis probabilistic neural network classifier |
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