In 3D facial expression recognition, operator algorithm based on LBP has the characteristics of accurate, precise, and invariable illumination, while it also has the disadvantages of high dimensions of histogram, poor discriminating ability, and large redundant information, comparing with traditional feature extraction method. In this study, a CPB algorithm is proposed based on multi-scaled block CBP feature extraction system for the classification of facial expressions that are represented in 3D, designating to extract features more efficiently. Then, sparse expression classifier is used to classify and identify the features. The experimental results demonstrate that the facial expression recognition rate has been greatly improved, comparing with traditional LBP algorithm and SVM algorithm.