.png){kind=icon}
返回
A Dynamic Multi-Scale Convolution Model for Face Recognition Using Event-Related Potentials
.png){kind=icon}
OA
AI
DOI：10.3390/s24134368.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
familiar and unfamiliar face recognition
mask
multi-scale
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Cross sectional study of young people's awareness of and involvement with tobacco marketing.png)
BMJOAAI
BMJIF0
OAAI分享收藏Using the three e’s (emphasis, expectations, and evaluation) to structure writing objectives for pharmacy practice experiences运用“三E”原则（即重点、期望和评估）来构建药学实践体验的写作目标
PREAI
分享收藏
查看更多内容