针对ASPCM模型处理转动角度较大的人脸图像时出现的不足，提出CASPCM模型。以样本与模型中心的距离为依据将训练样本分组，为每个分组训练ASPCM模型；将局部ASPCM模型的合成映射结果加权平均得到CASPCM模型的合成结果；提出利用梯度下降法使分解映射的姿势估计逐步精确。采用精确性和概括性两个标准衡量该模型的分解性能和合成性能。实验表明，CASPCM模型的分解性能和合成性能均优于ASPCM模型；基于该模型的人脸识别系统在处理转动角度较大的人脸图像时，识别率比ASPCM模型高7%。

CASPCM model is proposed to make up the disadvantages of ASPCM model while dealing with faces with large angles. The training samples are grouped according to their distances to model centers and a local ASPCM model is constructed for each group. Synthesis result of CASPCM model is obtained by averaging results of the local ASPCM models with appropriate weights. Gradient-descent algorithm is used to iteratively improve estimate of the head pose. Accuracy and generalization are used to gauge analysis and synthesis abilities of the model. Experimental results show that the two abilities of CASPCM model are both superior to ASPCM model;recognition ratio of CASPCM model is 7% higher than ASPCM model.