The ability to handle multi-view facial expressions is important for computers to understand affective behavior under less constrained environment. However, most of existing methods for facial expression recognition are based on the near-frontal view face data, which are likely to fail in the non-frontal facial expression analysis. In this paper, we conduct an investigation on analyzing multi-view facial expressions. Three local patch descriptors (HoG, LBP, and SIFT) are used to extract facial features, which are the inputs to a nearest-neighbor indexing method that identifies facial expressions. We also investigate the influence of feature dimension reductions (PCA, LDA, and LPP) and classifier fusion on the recognition performance. We test our approaches on multi-view data generated from BU-3DFE 3D facial expression database that includes 100 subjects with 6 emotions and 4 intensity levels. Our extensive person-independent experiments suggest that the SIFT descriptor outperforms HoG and LBP, and LPP outperforms PCA and LDA in this application. But the classifier fusion does not show a significant advantage over SIFT-only classifier.