Journal:IEEE Transactions on Biometrics, Behavior, and Identity Science
Key Words:robust dynamic facial expression recognition, noisy samples, hard samples, computational perception
Abstract:The study of Dynamic Facial Expression Recognition (DFER) is a nascent field of research that involves the automated recognition of facial expressions in video data. Although existing research has primarily focused on learning representations under noisy and hard samples, the issue of the coexistence of both types of samples remains unresolved. In order to overcome this challenge, this paper proposes a robust method of distinguishing between hard and noisy samples. This is achieved by evaluating the prediction agreement of the model on different sampled clips of the video. Subsequently, method
Note:论文链接:https://ieeexplore.ieee.org/document/10908623

Arxiv连接:http://arxiv.org/abs/2502.16129v1

引用 Feng Liu, Hanyang Wang, Siyuan Shen. Robust Dynamic Facial Expression Recognition[J]IEEE Transactions on Biometrics, Behavior, and Identity Science.2025.https://doi.org/10.1109/TBIOM.2025.3546279.
Discipline:工学
First-Level Discipline:Computer Science and Technology
Document Type:J
Translation or Not:no
Date of Publication:2025-03-03
Included Journals:SCI、EI
Indexed by:[J]