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Abstract：
With the development of data mining technology, the analysis of event-related potential (ERP) data has evolved from statistical analysis of time-domain features to data-driven techniques based on supervised and unsupervised learning. However, there are still many challenges in understanding the relationship between ERP components and the representation of familiar and unfamiliar faces. To address this, this paper proposes a model based on Dynamic Multi-Scale Convolution for group recognition of familiar and unfamiliar faces. This approach uses generated weight masks for cross-subject familiar/unfamiliar face recognition using a multi-scale model. The model employs a variable-length filter generator to dynamically determine the optimal filter length for time-series samples, thereby capturing features at different time scales. Comparative experiments are conducted to evaluate the model\'s performance against SOTA models. The results demonstrate that our model achieves impressive outcomes, with a balanced accuracy rate of 93.20% and an F1 score of 88.54%, outperforming the methods used for comparison. The ERP data extracted from different time regions in the model can also provide data-driven technical support for research based on the representation of different ERP components.
摘要：
随着数据挖掘技术的发展,事件相关电位（ERP）数据的分析已经从时域特征的统计分析发展到基于监督和无监督学习的数据驱动技术。然而,在理解ERP组件与熟悉和陌生面孔的表示之间的关系方面仍然存在许多挑战。为了解决这个问题，本文提出了一种基于动态多尺度卷积的熟悉和陌生人脸群识别模型。该方法使用生成的权重掩模用于使用多尺度模型的跨主题熟悉/不熟悉的面部识别。该模型采用可变长度滤波器生成器来动态确定时间序列样本的最佳滤波器长度，从而捕获不同时间尺度的特征。进行了比较实验，以评估模型与SOTA模型的性能。结果表明，我们的模型取得了令人印象深刻的成果，平衡准确率为93.20%，F1评分为88.54%，优于用于比较的方法。模型中从不同时间区域提取的ERP数据也可以为基于不同ERP组件表示的研究提供数据驱动的技术支持。
