The Latest Advances in Face Recognition with Single Training Sample

Face recognition system based on one gallery sample is very valuable for less laborious effort for collecting images and lowering the cost for storing and processing them. However, it is very challengeable to correctly recognize a person from face database with only one sample for everybody. Some algorithms to deal with one sample problem have been proposed in recent years. They are reviewed and introduced simply. The correct recognition rates in experiments of these algorithms are compared and the relevant issues such as database, class number, and how to divide training set and testing set are also discussed. Some key factors in one sample face recognition are pointed out and some promising directions for future research are also proposed.