Estimation of facial pain expressions is effective for pain assessment. In this study, facial pain is recognized through a feature extraction method integrating block weighted Local Binary Pattern (LBP) and multi-scale partition. First, the pre-processed image is weighted after the histogram is extracted in blocks. Then statistical features of histograms are extracted in multi-scale partitions to concatenate them with different sizes of blocks and cascade the block weighted histograms into the feature vector of the entire image. Finally, the Principal Component Analysis (PCA) is relied on to reduce the dimensionality of the feature vector, and the Support Vector Machine (SVM) is used for classification and recognition. The experiments on a self-built database of pain expression images prove that the proposed method, compared with traditional feature extraction methods and those before fusion, greatly improves the recognition rate of pain expressions. Then it can serve as an effective way for studying and recognizing pain expressions.