Principal Component Analysis (PCA) can effectively extract global features from images and has advantages of dimension reduction. During the dimension reduction process, because of the comparatively concentration of eigenvalues, the dimension is still larger than the best. To solve this problem, this paper presents the optimal-sample PCA (OS-PCA) for dimension reduction. By choosing the training samples and optimizing the covariance matrix, OS-PCA achieves the purpose of further dimension reduction. Because Discrete Cosine Transform (DCT) has robustness of light, as well as Local Binary Pattern (LBP) is effective in describing local texture features, the paper combines DCT and LBP features to make up for the limitations of OS-PCA in facial expression representation. In order to utilize the advantages of collaboration features and classifiers, this paper constructs a facial expression recognition model, which is based on three layers of the optimal integration of multiple classifiers. Firstly, facial images are preprocessed. This step includes the detection of face from images and normalization. Then the OS-PCA, DCT and LBP features are delivered into the model. Finally, based on the best match combination between single classifier and single feature, the model completes the optimal integration of multiple features and multiple classifiers. Via voting mechanism, the model makes adaptive decisions for images that are still different to get the final recognition result. Experiments show that OS-PCA is more effective than PCA in dimension reduction. On the JAFFE and CK database, recognition rates are higher than 95% and 96%, and the proposed model shows brilliant time performance.