The recognition accuracy rate of traditional Sparse Representation Classification (SRC) algorithm is relatively low under the interference of complex non-face ingredient, large training sample set and high similarity between the training samples. To solve these problems, a novel face recognition algorithm based on Cluster-Sparse of Active Appearance Model (CS-AAM) was proposed. Firstly, Active Appearance Model (AAM) rapidly and accurately locate facial feature points and to get the main information of the face. Secondly, K-means clustering was run on the training sample set, the images with high similarity degree were assigned to a category and the clustering center was calculated. Then, the center was used as atomic to structure over-complete dictionary and do sparse decomposition. Finally, face images were classified and recognized by computing sparse coefficients and reconstruction residuals. The face images with different samples and different dimensions from ORL face database and Extended Yale B face database were tested for comparing CS-AAM with Nearest Neighbor (NN), Support Vector Machine (SVM), Sparse Representation Classification (SRC), and Collaborative Representation Classification (CRC). The recognition rate of CS-AAM algorithm is higher than other algorithms with the same samples or the same dimensions. Under the same dimensions, the recognition rate of CS-AAM is 95.2% when the selected number of samples is 210 on ORL face database; the recognition rate of CS-AAM is 96.8% when the selected number of samples is 600 on Extended Yale B face database. The experimental results demonstrate that the proposed method has higher recognition accuracy rate.