| 应用非负矩阵分解和RBPNN模型的掌纹识别方法 |
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| 引用本文： | 尚丽,崔鸣,杜吉祥.应用非负矩阵分解和RBPNN模型的掌纹识别方法[J].计算机工程与应用,2012,48(4):199-203. |
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| 作者姓名： | 尚丽 崔鸣 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,215104
2. 中国科学技术大学自动化系,合肥230026;华侨大学计算机科学与技术系,福建泉州362021 |
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| 基金项目： | 国家自然科学基金（No.60970058）;中国博士后科学基金资助项目（No.20060390180,200801231）;江苏省自然科学基金项目（No.BK2009131）;江苏省“青蓝工程”资助项目;2010苏州市职业大学创新团队资助项目（No.3100125）. |
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| 摘 要： | 提出一种基于非负矩阵分解(NMF)和径向基概率神经网络的掌纹识别方法。NFM是一种有效的图像局部特征提取算法,用于图像分类时能得到较高的识别率。考虑PolyU掌纹图像数据库,应用NMF、局部NMF(LNMF)、稀疏NMF(SNMF)和具有稀疏度约束的NMF(NMFSC)算法分别对掌纹图像进行特征提取,并对提取到的局部特征基图像进行分析对比;在特征提取的基础上,应用径向基概率神经网络(RBPNN)模型对掌纹特征进行分类,分类结果表明了RBPNN模型对掌纹特征具有较好的识别能力。实验对比结果证明了基于RBPNN的NMF掌纹识别方法在掌纹识别中的有效性,具有一定的理论研究意义和实用性。
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| 关 键 词： | 非负矩阵分解 局部特征提取 特征基图像 掌纹识别 径向基概率神经网络(RBPNN)分类器 |
| 修稿时间： | |
Palmprint recognition methods using non-negative matrix factorization and RBPNN model |
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| SHANG Li
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CUI Ming
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DU Jixiang.Palmprint recognition methods using non-negative matrix factorization and RBPNN model[J].Computer Engineering and Applications,2012,48(4):199-203. |
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| Authors: | SHANG Li CUI Ming DU Jixiang |
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| Affiliation: | 1.Department of Electronic Information Engineering, Suzhou Vocational University, Suzhou, Jiangsu 215104, China2.Department of Automation, University of Science and Technology of China, Hefei 230026, China3.Department of Computer Science and Technology, Huaqiao University, Quanzhou, Fujian 362021, China |
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| Abstract: | A palmprint recognition method based on Non-negative Matrix Factorization （NMF） and Radial Basis Probabilistic Neural Network（RBPNN） is proposed. NMF is an efficient local feature extraction algorithm of images, and it can obtain high recognition rate in image classification task. Considered PolyU palmprint image database, the palm features are extracted by using several algorithms, such as NMF, Local NMF（LNMF）, Sparse NMF（SNMF）, and NMF with Sparseness Constraints（NMFSC） et al. And these feature ba- sis images extracted are analyzed and compared. On the basis of feature extraction, the RBPNN classifier is utilized to classify palm- print features, and the classification results show that the RBPNN model has better palmpriut recognition property. Compared classifica- tion results obtained by different algorithms, it is clear to see that the palmprint recognition results based on RBPNN and NMF are in- deed efficient, and these algorithms behave certain theory research meaning and application in practice. |
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| Keywords: | non-negative matrix factorization local feature extraction feature basis image palmprint recognition radial basis probabilistic neural networks classifier |
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