Related papers: End-to-End Facial Expression Detection in Long Videos

End-to-End Facial Expression Detection in Long Videos

URL: http://arxiv.org/abs/2504.07660v1
Date: Thu, 10 Apr 2025 11:18:46 GMT
Title: End-to-End Facial Expression Detection in Long Videos
Authors: Yini Fang, Alec Diallo, Yiqi Shi, Frederic Jumelle, Bertram Shi,
Abstract summary: We propose an end-to-end Facial Expression Detection Network (FEDN) to jointly optimize spotting and recognition.<n>By unifying two tasks within a single network, we greatly reduce error propagation and enhance overall performance.
Score: 0.2796197251957245
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Facial expression detection involves two interrelated tasks: spotting, which identifies the onset and offset of expressions, and recognition, which classifies them into emotional categories. Most existing methods treat these tasks separately using a two-step training pipelines. A spotting model first detects expression intervals. A recognition model then classifies the detected segments. However, this sequential approach leads to error propagation, inefficient feature learning, and suboptimal performance due to the lack of joint optimization of the two tasks. We propose FEDN, an end-to-end Facial Expression Detection Network that jointly optimizes spotting and recognition. Our model introduces a novel attention-based feature extraction module, incorporating segment attention and sliding window attention to improve facial feature learning. By unifying two tasks within a single network, we greatly reduce error propagation and enhance overall performance. Experiments on CASME}^2 and CASME^3 demonstrate state-of-the-art accuracy for both spotting and detection, underscoring the benefits of joint optimization for robust facial expression detection in long videos.

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