Papers › POSTER++: A simpler and stronger facial expression recognition network

POSTER++: A simpler and stronger facial expression recognition network

28 Jan 2023arXiv:2301.12149archive 2025-07-28

Jiawei Mao, Rui Xu, Xuesong Yin, Yuanqi Chang, Binling Nie, Aibin Huang

Facial expression recognition (FER) plays an important role in a variety of real-world applications such as human-computer interaction. POSTER achieves the state-of-the-art (SOTA) performance in FER by effectively combining facial landmark and image features through two-stream pyramid cross-fusion design. However, the architecture of POSTER is undoubtedly complex. It causes expensive computational costs. In order to relieve the computational pressure of POSTER, in this paper, we propose POSTER++. It improves POSTER in three directions: cross-fusion, two-stream, and multi-scale feature extraction. In cross-fusion, we use window-based cross-attention mechanism replacing vanilla cross-attention mechanism. We remove the image-to-landmark branch in the two-stream design. For multi-scale feature extraction, POSTER++ combines images with landmark's multi-scale features to replace POSTER's pyramid design. Extensive experiments on several standard datasets show that our POSTER++ achieves the SOTA FER performance with the minimum computational cost. For example, POSTER++ reached 92.21% on RAF-DB, 67.49% on AffectNet (7 cls) and 63.77% on AffectNet (8 cls), respectively, using only 8.4G floating point operations (FLOPs) and 43.7M parameters (Param). This demonstrates the effectiveness of our improvements.

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Tasks

Facial Expression RecognitionFacial Expression Recognition (FER)

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Facial Expression Recognition (FER) AffectNet POSTER++ Accuracy (7 emotion) 67.49 #8 of 50 Archive leaderboard report
Facial Expression Recognition (FER) AffectNet POSTER++ Accuracy (8 emotion) 63.77 #8 of 50 Archive leaderboard report
Facial Expression Recognition (FER) RAF-DB POSTER++ Overall Accuracy 92.21 #10 of 35 Archive leaderboard report

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