The invention relates to a face recognition method that suits for the small sample, which uses two-dimensional matrix, to establish sample data matrix, similar member matrix, getting typical correlation matrix and finally generalized inverse matrix. It solves the problem that the queer covariance matrix is added artificial disturbance to process correction, which brings disturbance data introducing more random and uncertainty, making the result of small sample is not satisfied, leading to a significant increase in calculation, discarding the image null space ahead due to the method of pre-treating samples by dimensionality reduction, losing some important distinguishing information. The invention replaces number 1 with a matrix, that is the same size as sample image, to make the similar member matrix not only reflect the affiliation on each sample and any kind, but retain the space information of rows and columns in the sample images. |