由于足迹数据的样本量有限,类间差小、类内距大,一般方法难以获取有效的足迹特征表示,导致足迹分类准确度不高。针对双模态2D足迹分类问题,文中提出一种基于小样本学习的多模块网络算法（MulRN）,该算法在嵌入单元与关系单元使用了多个模块来提高网络的特征提取能力与特征度量能力,使用具有多分支结构的Inception模块与MRFB模块提升网络的特征提取能力,采用空间注意力模块与通道注意力模块提取出区分度更高的足迹特征,从而更好地实现足迹分类;并在miniImageNet、Omniglot等小样本数据集与双模态2D足迹数据集上进行了实验。实验结果表明,该方法在小样本数据集上具有较好的表现,同时在双模态2D足迹数据集上也达到了不错的效果,特别在右脚双模态数据集上的5-way 5-shot实验中达到了95.41%的分类准确率。

Due to the limited samples of footprint data and its high similarity between types and large gap within a type, there is no effective method to express footprint data and classify footprint. In order to solve the problem of bimodal footprint classification, the multiple-module relation network (MulRN) based on few-shot learning was proposed in this paper. Multiple modules were applied in the algorithm to improve the ability of extraction and mea-surement of characters. Inception module and MRFB module which possess a multi-branch structure were used to improve the character extraction ability. Spatial Attention Module (SAM) and Channel Attention Module (CAM) were adopted to extract the character of footprint with high discrimination for accurate classification. Also, experiments were carried out on few-shot data sets such as miniImageNet, Omniglot and bimodal 2D footprint data sets. Experimental results show that the proposed method is effective for few-shot data sets and bimodal 2D footprint data sets. It is worth mentioning that the accuracy of 5-way 5-shot experiment on bimodal data sets of right foot is up to 95.41%.