Pairwise dependency-based robust ensemble pruning for facial expression recognition
Author:
Funder
Science and Technology Program of Guizhou Province
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16756-1.pdf
Reference44 articles.
1. Sun N, Song Y, Liu J, Chai L, Sun H (2023) Appearance and geometry transformer for facial expression recognition in the wild. Comput Electr Eng 107:108583. https://doi.org/10.1016/j.compeleceng.2023.108583
2. Li X et al (2023) Facial expression recognition network with slow convolution and zero-parameter attention mechanism. Optik 283:170892. https://doi.org/10.1016/j.ijleo.2023.170892
3. Li T, Chan K-L, Tjahjadi T (2023) Multi-scale correlation module for video-based facial expression recognition in the wild. Pattern Recognit 142:109691. https://doi.org/10.1016/j.patcog.2023.109691
4. Li D, Wen G, Li X, Cai X (2019) Graph-based dynamic ensemble pruning for facial expression recognition. Appl Intell 49:3188–3206. https://doi.org/10.1007/s10489-019-01435-2
5. Li D, Wen G (2018) Mrmr-based ensemble pruning for facial expression recognition. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-5105-z
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