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Facial expression (FE) analysis plays a crucial role in various fields, such as affective computing, marketing, and clinical evaluation. Despite numerous advances, research on FE recognition (FER) has recently been proceeding from confined laboratory circumstances to in-the-wild environments. FER is still an arduous and demanding problem due to occlusion and pose changes, intraclass and intensity variations caused by illumination, and insufficient training data. Most state-of-the-art (SOTA) approaches use the entire face for FER. However, past studies on psychology and physiology reveal that the mouth and eyes reflect the variations of various FEs, which are closely related to the manifestation of emotion. A novel method is proposed in this study to address some of the issues mentioned above. First, modified homomorphic filtering (MHF) is employed to normalize the illumination, then the normalized face …