Palmprint recognition based on two-dimensional methods

Computer-aided personal recognition is becoming increasingly important in our information society. Human palmprint recognition has become an active area of research over the last decade. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are widely used in the field of palmprint recognition. However, the conventional PCA and LDA are both based on vectors. It means that the two-dimensional (2D) palmprint image matrices must be transformed into one-dimensional (ID) image vectors previously. The resulting image vectors of palmprint usually lead to a high dimensional image vector space. In this paper, two-dimensional PCA and LDA are used in palmprint recognition. Unlike conventional PCA and LDA that treat image as vectors, the 2D methods view an image as a matrix directly. The experimental results on our palmprint database show that two-dimensional PCA and LDA can obtain over 99% recognition rate in palmprint verification, while using less time and memory. They are more effective than conventional PCA and LDA in terms of accuracy and efficiency.