This paper proposes to leverage multiple facets of person photos to improve the training of deep neural networks. Existing studies usually require a lot of labeled images to train deep convolutional networks. Our study suggests exploring multiple datasets and learning effective representation to learn related visual concepts. The practice of learning from multiple facets implicitly enforces to share features for image recognition. We show deep neural network benefits from the learning of multiple person-related categories in photos. Faceted classification systems learn from multiple resources, and alleviate the overfitting problems in deep learning. Moreover, by exploring multiple taxonomies of an object, it provides a finer annotation for the query images.