An important task of affective computing is to build computable emotional models. In our study

PAD theory is used and EBM (emotional block model)is built and verified in facial emotion recognition area. 88 points based Gabor feature and SVM (support vector machine) classifier are used to verify this model on Cohn-Kanade dataset. Non-basic and basic emotions are recognized with EBM model in our experiment

and the advantage and disadvantage are compared with PAD based models and traditional basic emotional models. Experimental results show that EBM is reliable. The result is better in high-convergent emotional block than in low-convergent emotional block.