GABOR-BASED TENSOR LOCAL DISCRIMINANT EMBEDDING AND ITS APPLICATION ON PALMPRINT RECOGNITION

摘要

In this paper, a novel feature extraction method called Gabor-based tensor local discriminant embedding (GTLDE) is proposed. GTLDE first gets the high-order statistic information by using a biologically inspired hierarchical model, and then tensor local discriminant embedding (TLDE) is carried out to extract the discriminant features of the image for recognition task. The method we proposed is not only robust to local translation and scale variations, but also has high distinguishing ability. More importantly, our method can achieve high accuracy with a small number of training samples. Experimental results on PolyU-II palmprint database demonstrate the effectiveness of the method we proposed.