Facial Expression Recognition Under Partial Occlusion Using Part-Based Ensemble Learning

Facial expression recognition (FER) under partial occlusion remains a challenging task, especially when key regions of the face, such as the mouth and nose, are covered by medical masks. Such conditions significantly reduce the discriminative features available for accurate emotion recognition, limiting the effectiveness of conventional full-face approaches. To address this issue, this study proposes a part-based learning framework that partitions the face into multiple regions, allowing the model to exploit unoccluded areas for expression recognition. The proposed method employs Support Vector Machine (SVM) classifiers trained on Histogram of Oriented Gradients (HoG) features extracted from 2, 3, 4, and 6 facial partitions. Each part-based model is trained independently, and their outputs are combined through a weighted soft voting ensemble mechanism to generate the final prediction. The experiments were conducted on the MaskedFER2013 dataset, which contains 31,116 grayscale facial images (48×48 pixels) distributed across seven emotion classes. The results demonstrate that the four-part model achieves the best performance, reaching an accuracy of 45%, outperforming both single-part models and full-face baselines under occlusion scenarios. These findings confirm that the proposed part-based ensemble approach enhances the robustness of FER systems by effectively leveraging complementary regional features, thereby providing a promising solution for real-world applications, where facial occlusion is unavoidable.