We address the problem of one-shot unconstrained face recognition. This is addressed by using a deep attribute representation of faces. While face recognition has considered the use of attribute based representations, for one-shot face recognition, the methods proposed so far have been using different features that represent the limited example available. We postulate that by using an intermediate attribute representation, it is possible to outperform purely face based feature representation for one-shot recognition. We use two one-shot face recognition techniques based on exemplar SVM and one-shot similarity kernel to compare face based deep feature representations against deep attribute based representation. The evaluation on standard dataset of ‘Labeled faces in the wild’ suggests that deep attribute based representations can outperform deep feature based face representations for this problem of one-shot face recognition.