用注意力编码器和原型网络实现少样本司机识别，精度超99%且模型更轻量。
A Prototypical Network with an Attention-based Encoder for Drivers Identification Application
- 结合原型网络与注意力编码器，支持少样本学习。
- 在三个数据集上准确率达99.3%~99.9%，参数减少87.6%。
- 适用于数据稀缺或未知司机场景，适合车载安全系统部署。
司机识别近年来日益受到关注，尤其在数据驱动应用中，因生物特征技术可能引发隐私问题。本文提出一种基于注意力机制的编码器（AttEnc）深度神经网络架构，相比现有方法参数更少。多数研究未解决司机识别中的数据短缺问题，且对未知司机适应性差。为此，本文设计了原型网络与注意力编码器结合的P-AttEnc架构，利用少样本学习缓解数据不足并提升泛化能力。实验表明，注意力编码器在三个不同数据集上的识别准确率分别达99.3%、99.0%和99.9%，预测时间比基准快44%至79%，平均参数减少87.6%。P-AttEnc在单样本场景下识别准确率为69.8%，在1-shot条件下对未知司机分类平均准确率达65.7%。该方法可从少量数据中提取司机指纹，有效应对数据稀缺问题。
原文摘要 · Abstract (English)
Driver identification has become an area of increasing interest in recent years, especially for data- driven applications, because biometric-based technologies may incur privacy issues. This study proposes a deep learning neural network architecture, an attention-based encoder (AttEnc), which uses an attention mechanism for driver identification and uses fewer model parameters than current methods. Most studies do not address the issue of data shortages for driver identification, and most of them are inflexible when encountering unknown drivers. In this study, an architecture that combines a prototypical network and an attention-based encoder (P-AttEnc) is proposed. It applies few-shot learning to overcome the data shortage issues and to enhance model generalizations. The experiments showed that the attention-based encoder can identify drivers with accuracies of 99.3%, 99.0% and 99.9% in three different datasets and has a prediction time that is 44% to 79% faster because it significantly reduces, on average, 87.6% of the model parameters. P-AttEnc identifies drivers based on few shot data, extracts driver fingerprints to address the issue of data shortages, and is able to classify unknown drivers. The first experiment showed that P-AttEnc can identify drivers with an accuracy of 69.8% in the one-shot scenario. The second experiment showed that P-AttEnc, in the 1-shot scenario, can classify unknown drivers with an average accuracy of 65.7%.
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