When each person has only one training sample,the recognition perform ance of maximum scatter difference (MSD) discriminant analysis in face recognition would be reduced.To solve this problem,a single s ample face recognition algorithm based on sample augments and MSD fusion is proposed in this paper.First,according to the facia l symmetry theory,some relevant information of possible change could be extracted to adapt to the future samples.At the same t ime,the average face may also have some changes of the test faces in the future.So the original training sample,symmetrical virtual f ace and average virtual face are combined to a new training sample set.Then,the MSD algorithm is performed on the new training sample set to get the optimal projection matrix.Therefore,the features of the training sample and testing fa cial images could be obtained by projecting them on to the optimal projection matrix achieved above.During the recognition stage,the fuzzy decision is used to do the classification.Extensive experiment results on famous ORL and FERET show that the algorithm can improve t he recognition rate and has certain robustness.