Palmprint recognition based on invariant moments

Feature extraction is one of the most basic problems in palmprint recognition.For palmprint image recognition,extracting the effective classifying feature of the palmprint images is a crucial step.The essence of palmprint feature extraction is to reduce the high-dimensional original palmprint image to a low-dimensional feature subspace that is benefit to classifying the palmprint.The palmprint classifying feature is extracted by using.Then the Palmprint images are recognized by adopting the K-nearest neighborhood classifier.Experiments on the public palmprint dataset validate the effectiveness and feasibility of the proposed algorithm.