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Small sample set, occlusion, and illumination variations are the critical obstacles for a face identification system towards practical application. In this paper, we propose a probabilistic generative model for parts-based data representation to address these difficulties. In our approach, multiple subcategories corresponding to the individual face parts, such as nose, mouth, eye, and so forth, are modeled within a probabilistic graphical model framework to mimic the process of generating a face image. The induced face representation, therefore, encodes rich discriminative information. Model training is totally unsupervised. Once the training is completed, a test sample from the face class can be recognized as a novel combination of learned parts. In summary, the main contributions of this work are threefold: 1) A novel hierarchical probabilistic generative model is proposed, which is capable of achieving an efficient parts-based representation for robust face identification. 2) A constrained variational EM algorithm is developed to learn the model parameters and infer the variables. 3) Two similarity metrics are specially designed for the novel parts-based feature representation, which are effective for matching score guided one sample face identification. The models and similarity metrics are validated on three face databases. Experimental results demonstrate the capabilities of the model to deal with small sample set, occlusions, and illumination variances.