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Face recognition with single sample per person (SSPP) was a very challenging task because SSPP cannot reflect the characteristics of the test samples (eg. illuminations, pose, expressions and disguises) so that it is difficult to predict the facial variations of a query sample by the gallery samples. Considering the fact that the intra-class facial variations can be shared across different subjects, a novel algorithm was put forward. This algorithm took generic variation dictionary to solve the problem of lack the number of sample. This method need create two different dictionary. Generic variation dictionary D was built by the different group of pictures and gallery dictionary X was built from the training samples. Using half-quadratic optimization function to solve optimization problem. The experimental results showed that the recognition rate of the algorithm was superior to recognition rate of some traditional methods.

Video Face Recognition based on External General Dictionary