Single sample face recognition (SSFR) is a challenging research problem in which only one face image per person is available for training. Moreover, the face image may have different pose, expression, illumination, occlusion etc. rendering this problem more complex. Several methods have been suggested by various researchers in literature to solve SSFR. Here, we provide a comprehensive review of the methods proposed in the last decade for solving SSFR problem and introduce a novel taxonomy for the same. We divide SSFR methods broadly into five categories viz. (i) feature based, (ii) virtual sample generation based, (iii) generic database based, (iv) Hybrid and (v) other methods. We have also briefly reviewed the face databases used for evaluating single sample face recognition methods. Furthermore, the performance of the methods has been analyzed in terms of classification accuracy as given in literature. At last, we also suggest some future direction to the researchers and practitioners working in this fascinating research area.