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摘要:
掌纹因稳定性、唯一性、难复制性及易获取等特点,是极具应用潜力的生物识别特征。针对掌纹识别中,获取掌纹感兴趣区域(ROI)与增强掌纹信息普遍存在的时间成本大,方法之间有依赖关系等问题,提出了一种基于U-Net神经网络结构的掌纹图像预处理方法。通过香港理工大学掌纹库进行实验对比,结果表明,所提方法能消除预处理方法之间的相互影响,实现对掌纹图像的去噪与增强处理,并能快速、高精度提取感兴趣区域。
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关键词:
- U-Net /
- 感兴趣区域(ROI)增强 /
- 深度学习 /
- 感兴趣区域(ROI)提取 /
- 掌纹
Abstract:Palmprint is a biometric feature with great application potential due to its stability, uniqueness, hardness in copying, and easy access.In the palmprint recognition, palmprint Region of Interest(ROI) acquisition and palmprint enhancement usually have the problems of high time cost and high dependency between methods.This paper proposes a palmprint preprocessing method based on U-Net neural network structure.The experiments are carried out on palmprint database from Hong Kong Polytechnic University.The results show that the proposed method can eliminate the mutual influence between the preprocessing methods.Both denoising and enhancement of palmprint images are realized, and the region of interest can be extracted quickly and accurately; plamprint
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表 1 U-Net神经网络模型参数
Table 1. U-Net neural network model parameters
层类型 输入大小 滤波器大小 重复次数 卷积模块 1×384×280 3×3 1 卷积模块 64×384×280 3×3 1 池化层 64×384×280 2×2 1 卷积模块 64×192×140 3×3 2 池化层 128×192×140 2×2 1 卷积模块 128×96×70 3×3 2 池化层 256×96×70 2×2 1 卷积模块 256×48×35 3×3 2 上采样层 512×48×35 3×3 1 卷积模块 512×96×70 3×3 2 上采样层 256×96×70 3×3 1 卷积模块 256×192×140 3×3 2 上采样层 128×192×140 3×3 1 卷积模块 128×384×280 3×3 1 卷积模块 64×384×280 3×3 1 卷积模块 64×384×280 1×1 1 表 2 掌纹增强效果比较结果
Table 2. Comparison results of palmprint enhancement
方法 BM3D去噪+Gabor小波增强后为真值 OBLNM增强后为真值 PSNR Entropy AMBE PSNR Entropy AMBE BM3D去噪+Gabor小波增强 24.727 6 6.435 6 3.163 0 24.049 3 6.531 6 4.168 1 OBLNM 23.171 6 6.916 0 12.419 8 26.334 8 6.877 0 5.930 9 RKTFILT 23.499 4 6.820 0 15.239 3 28.022 2 6.753 7 6.806 8 SRAD 26.925 8 6.005 4 7.346 4 27.037 8 6.154 6 5.653 2 本文方法 27.120 8 7.247 6 3.165 3 27.916 9 6.920 7 3.210 1 表 3 掌纹感兴趣区域重合度分析
Table 3. Analysis of coincidence degree of palmprint ROI
方法 面积重合率/% 方法1 91.18 方法2 88.00 本文方法 94.00 表 4 掌纹分割图像相似度对比
Table 4. Similarity comparison of palmprint segmentation images
方法 Cosin相似度/% 哈希相似度/% 方法1 99.32 73.17 方法2 99.13 71.61 本文方法 99.49 76.47 表 5 每张掌纹图像处理时间
Table 5. Processing time of each palmprint image
方法 时间/ms 增强 分割 增强和分割 方法1 1 355.73 32.23 1 387.96 方法2 1 355.73 14.49 1 370.22 本文方法 90.49 -
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