| 摘要: |
| 针对获取的手指静脉图像不仅包含静脉特征，而且包含噪声和不规则阴影，从而增加了特征提取难度的问题，提出了一种基于稀疏自编码的手指静脉图像分割算法；首先采用传统分割算法对原始灰度图像进行分割，得到一副二值图像（背景像素值为0，静脉像素值为1）；然后，以该灰度图像的每个像素点为中心，对其进行图像分块，并将二值图像中对应于中心点的值（0或者1）作为该块的标签，建立训练集合；最后，将训练样本（分块图像和标签）输入到自编码器和神经网络中进行训练，再用训练好的模型对测试图像进行分割；实验结果表明，相比传统的算法，提出的手指静脉分割算法能够有效地对静脉进行分割，提高手指静脉认证系统的认证精度。 |
| 关键词: 手指静脉 图像分割 稀疏自编码器 |
| DOI： |
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| Finger Vein Image Segmentation Based on Sparse Auto-Encoder |
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QIN Hua-feng, LIU Xia
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| Abstract: |
| According to that the acquired finger-vein images contain not only vein features, but also noise and irregular shadows, increasing the difficulty of feature extraction, this paper proposes a novel method for finger-vein image segmentation based on sparse self-encoding. Firstly, the original grayscale image is segmented by the traditional segmentation algorithm to obtain a binary image (the background pixel value is 0 and the vein pixel value is 1). Then, we take each pixel as center point to generate patch from original grayscale image and the value (0 or 1) of the corresponding point in the binary image is used as its label.A training set is built based on generated patches and labels. Finally, the patch images and labels are input into the auto-encoder and neural network for training, and the trained models are used to segment the test images. The experimental results show that compared with the traditional algorithm, the proposed finger vein segmentation algorithm can effectively segment the vein and improve the authentication accuracy of the finger vein authentication system. |
| Key words: finger vein image segmentation sparse auto-encoder |
