Over the past few decades, facial expression recognition (FER) has been widely deployed in real-world applications. However, the collection conditions of existing datasets vary substantially, leading to significant domain shifts among datasets. Consequently, the performance of the most advanced FER methods will deteriorate in cross-domain scenarios. To address this issue, we propose a Dual-Stream Feature Disentanglement Network (DFDNet) within the Single Domain Generalization (SDG) paradigm. DFD-Net employs the Expression Feature Extraction (EFE) module together with an attention block as the expression feature extraction branch, performing primary feature fusion and high-level feature selection. In parallel, the Expression-Irrelevant Feature Extraction (EIFE) module and the Expression-Irrelevant Feature Predictor (EIFP) constitute another branch. EIFE is pre-trained to capture the expression-irrelevant feature. EIFP passes the expression features through the Gradient Reversal Layer (GRL) and the Mutual Information Predictor (MIP) to compute and minimize the mutual information with the expression-irrelevant features. Extensive experiments on multiple benchmark datasets demonstrate that our method consistently outperforms existing state-of-the-art methods.