Image Recognition Based on Nonlinear Dimensionality Reduction

By transforming each image to high-dimension data set and the nonlinear dimension reduction, the 1-dimension result on the structure of the data manifold is acquired, which can be used to describe the image sufficiently. Consequently, the recognition result will be translated into the 1-dimension result. That will greatly reduce the calculative complexity and the identification error, which comes from the data redundancies, and increase the precision. At last, the example of the fingerprints shows that it is feasible and valid to apply the nonlinear dimension reduction to the image recognition.