A Dynamic Multi-Scale Convolution Model for Face Recognition Using Event-Related Potentials
文献类型：期刊论文
| 作者 | Li, Shengkai1,2; Zhang, Tonglin2,3 ; Yang, Fangmei2; Li, Xian1,4; Wang, Ziyang2; Zhao, Dongjie1,4
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| 刊名 | SENSORS
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| 出版日期 | 2024-07-01 |
| 卷号 | 24期号:13页码:17 |
| 关键词 | familiar and unfamiliar face recognition mask multi-scale |
| DOI | 10.3390/s24134368 |
| 通讯作者 | Wang, Ziyang(ziyang.wang@ia.ac.cn) ; Zhao, Dongjie(dongjiezhao@qdu.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | NEURAL-NETWORKS ; SELF-FACE ; CLASSIFICATION ; INFORMATION ; FAMILIAR ; SUBJECT ; COMPLEX ; LATENCY ; BRAIN |
| 资助项目 | National Natural Science Foundation of China[61379099] |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:001269772000001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/59257]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
| 通讯作者 | Wang, Ziyang; Zhao, Dongjie |
| 作者单位 | 1.Qingdao Univ, Sch Automat, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artifcial Intelligence Sy, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 4.Qingdao Univ, Shandong Key Lab Ind Control Technol, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Shengkai,Zhang, Tonglin,Yang, Fangmei,et al. A Dynamic Multi-Scale Convolution Model for Face Recognition Using Event-Related Potentials[J]. SENSORS,2024,24(13):17. |
| APA | Li, Shengkai,Zhang, Tonglin,Yang, Fangmei,Li, Xian,Wang, Ziyang,&Zhao, Dongjie.(2024).A Dynamic Multi-Scale Convolution Model for Face Recognition Using Event-Related Potentials.SENSORS,24(13),17. |
| MLA | Li, Shengkai,et al."A Dynamic Multi-Scale Convolution Model for Face Recognition Using Event-Related Potentials".SENSORS 24.13(2024):17. |
入库方式： OAI收割
来源：自动化研究所
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