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As face is a topological object, spatial contents contained in facial images (i.e. eyes, nose...) play an important role in feature extraction. To preserve spatial information, region decomposition is an essential step in face recognition for local feature based methods. In this paper, a new region decomposition method is proposed based on cellular neural network (CNN). This method, called face penta-chotomy (FPC), can be factorized into two parts. First, a stable facial region is extracted by a CNN template. Then other four regions are depicted according to the stable facial region and facial proportion. The local binary pattern (LBP) is adopted as the region descriptor. This method is evaluated by conducting experiments on the Yale face database B and ORL database. Besides, it compared with six state-of-the-art methods. From experimental results, it outperforms all the compared methods and the feature dimension can be significantly reduced compared with the conventional uniform region decomposition method. Moreover, the proposed method is demonstrated to be robust under single training condition.