论文标题
通过多任务损失功能和注意力层识别棕榈静脉
Palm Vein Recognition via Multi-task Loss Function and Attention Layer
论文作者
论文摘要
随着个人设备的算术能力和算法精度的提高，生物学特征越来越广泛地用于个人识别，并且棕榈静脉识别具有丰富的提取特征，并且近年来已经广泛研究了。但是，传统的识别方法的稳健性很差，并且容易受到环境影响，例如反射和噪音。在本文中，基于VGG-16转移学习融合注意机制的卷积神经网络被用作红外棕榈静脉数据集上的特征提取网络。首先，使用Palmprint分类方法对Palm静脉分类任务进行培训，然后使用相似性函数进行匹配，在该功能中，我们提出了多任务损耗函数，以提高匹配任务的准确性。为了验证模型的鲁棒性，在来自不同来源的数据集上进行了一些实验。然后，我们使用K-均值聚类来确定自适应匹配阈值，并最终在预测集上达到了98.89％的准确率。同时，匹配的效率很高，平均每个棕榈静脉对需要0.13秒，这意味着我们的方法可以在实践中采用。
With the improvement of arithmetic power and algorithm accuracy of personal devices, biological features are increasingly widely used in personal identification, and palm vein recognition has rich extractable features and has been widely studied in recent years. However, traditional recognition methods are poorly robust and susceptible to environmental influences such as reflections and noise. In this paper, a convolutional neural network based on VGG-16 transfer learning fused attention mechanism is used as the feature extraction network on the infrared palm vein dataset. The palm vein classification task is first trained using palmprint classification methods, followed by matching using a similarity function, in which we propose the multi-task loss function to improve the accuracy of the matching task. In order to verify the robustness of the model, some experiments were carried out on datasets from different sources. Then, we used K-means clustering to determine the adaptive matching threshold and finally achieved an accuracy rate of 98.89% on prediction set. At the same time, the matching is with high efficiency which takes an average of 0.13 seconds per palm vein pair, and that means our method can be adopted in practice.
