Robust Dynamic Facial Expression Recognition

• URL: http://arxiv.org/abs/2502.16129v1
• Date: Sat, 22 Feb 2025 07:48:12 GMT
• Title: Robust Dynamic Facial Expression Recognition
• Authors: Feng Liu, Hanyang Wang, Siyuan Shen,
• Abstract summary: This paper proposes a robust method of distinguishing between hard and noisy samples.<n>To identify the principal expression in a video, a key expression re-sampling framework and a dual-stream hierarchical network is proposed.<n>The proposed method has been shown to outperform current State-Of-The-Art approaches in DFER.
• Score: 6.626374248579249
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, methodologies that reinforce the learning of hard samples and mitigate the impact of noisy samples can be employed. Moreover, to identify the principal expression in a video and enhance the model's capacity for representation learning, comprising a key expression re-sampling framework and a dual-stream hierarchical network is proposed, namely Robust Dynamic Facial Expression Recognition (RDFER). The key expression re-sampling framework is designed to identify the key expression, thereby mitigating the potential confusion caused by non-target expressions. RDFER employs two sequence models with the objective of disentangling short-term facial movements and long-term emotional changes. The proposed method has been shown to outperform current State-Of-The-Art approaches in DFER through extensive experimentation on benchmark datasets such as DFEW and FERV39K. A comprehensive analysis provides valuable insights and observations regarding the proposed agreement. This work has significant implications for the field of dynamic facial expression recognition and promotes the further development of the field of noise-consistent robust learning in dynamic facial expression recognition. The code is available from [https://github.com/Cross-Innovation-Lab/RDFER].

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