PDF(Pubmed)
Abstract：
To address several common problems of finger vein recognition, a lightweight finger vein recognition algorithm by means of a small sample has been proposed in this study. First of all, a Gabor filter is applied to deal with the images for the purpose of that these processed images can simulate a kind of situation of finger vein at low temperature, such that the generalization ability of the algorithm model can be improved as well. By cutting down the amount of convolutional layers and fully connected layers in VGG-19, a lightweight network can be given. Meanwhile, the activation function of some convolutional layers is replaced to protect the network weight that can be updated successfully. After then, a multi-attention mechanism is introduced to the modified network architecture to result in improving the ability of extracting important features. Finally, a strategy based on transfer learning has been used to reduce the training time in the model training phase. Honestly, it is obvious that the proposed finger vein recognition algorithm has a good performance in recognition accuracy, robustness and speed. The experimental results show that the recognition accuracy can arrive at about 98.45%, which has had better performance in comparison with some existing algorithms.
摘要：
为了解决手指静脉识别的几个常见问题,本研究提出了一种基于小样本的轻量级手指静脉识别算法。首先,为了使处理后的图像能够模拟手指静脉在低温下的一种情况,从而提高算法模型的泛化能力。通过减少VGG-19中卷积层和全连接层的数量，可以给出一个轻量级网络。同时,部分卷积层的激活函数被替换,以保护能够成功更新的网络权值。在那之后,在改进的网络体系结构中引入了多注意力机制，以提高提取重要特征的能力。最后,基于迁移学习的策略被用来减少模型训练阶段的训练时间。老实说,很明显,本文提出的手指静脉识别算法在识别精度上有很好的表现,鲁棒性和速度。实验结果表明,识别准确率达到98.45%左右,与现有的一些算法相比，具有更好的性能。
