加权自适应CS-LBP与局部判别映射相结合的掌纹识别方法
提取掌纹的最佳低维分类特征一直是掌纹识别研究领域的一个重要方向。针对掌纹图像具有丰富的纹理特征特点，提出一种基于加权自适应中心对称局部二值模式（WACS—LBP）与局部判别映射（LDP）相结合的掌纹识别方法。首先将掌纹感兴趣（ROI）图像分成大小均匀的小区域，利用自适应中心对称局部二值模式（ACS—LBP）算法获取不同区域的纹理特征直方图和权值，经过加权连接得到ROI的加权纹理特征直方图向量；再利用LDP算法对得到的特征向量进行维数约简；最后利用K最近邻分类器进行掌纹识别。在掌纹公开数据库上进行实验，正确识别率高达97％以上。实验结果表明，该方法不仅是有效、可行的，而且研究思路比较明确。...
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| Published in | 计算机应用研究 Vol. 34; no. 11; pp. 3482 - 3485 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
郑州大学西亚斯国际学院,郑州,451150
2017
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| Subjects | |
| Online Access | Get full text |
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| Summary: | 提取掌纹的最佳低维分类特征一直是掌纹识别研究领域的一个重要方向。针对掌纹图像具有丰富的纹理特征特点，提出一种基于加权自适应中心对称局部二值模式（WACS—LBP）与局部判别映射（LDP）相结合的掌纹识别方法。首先将掌纹感兴趣（ROI）图像分成大小均匀的小区域，利用自适应中心对称局部二值模式（ACS—LBP）算法获取不同区域的纹理特征直方图和权值，经过加权连接得到ROI的加权纹理特征直方图向量；再利用LDP算法对得到的特征向量进行维数约简；最后利用K最近邻分类器进行掌纹识别。在掌纹公开数据库上进行实验，正确识别率高达97％以上。实验结果表明，该方法不仅是有效、可行的，而且研究思路比较明确。 |
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| Bibliography: | 51-1196/TP Extracting the optimal low-dimensionality classifying features from palmprint is always an important research area in the palmprint recognition field. On the basis of the rich texture features of palmprint image, this paper proposed a palmprint recognition method by combining weighted adaptive CS-LBP and local discriminant projection（LDP）. Firstly, this method di- vided the region of interest （ROI） image of patmprint into blocks with uniform size, and computed the texture feature histogram and weighted value of each block by the adaptive CS-LBP（ACS-LBP） algorithm. Then it obtained the weighted texture feature histogram vector by connecting all weighted histograms serially. Secondly, it reduced the feature vector by the local discriminant projection algorithm. Finally, it used the K-nearest neighborhood classifier to perform the palmprint identification. The proposed method was tested and compared with the existing algorithms on a public palmprint database. The recognition rate is over 97%. The exper |
| ISSN: | 1001-3695 |
| DOI: | 10.3969/j.issn.1001-3695.2017.11.063 |