Online Writer Identification Using The Point Distribution Model

Abstract

Due to the seemingly uniqueness of physiological and behavioral characteristics of each individual, writer identification has shown to be a feasible task. Security is the primary reason to perform any kind of biometrics-related personal identification. There have been scarce research results for personal identification using online Chinese handwriting. In this paper, we present a novel approach for online writer identification based on the point distribution model (PDM). The PDM technique provides a means to describe the variations in shapes in a parametric form. As a statistical tool, the PDM combines the benefits of feature alignment and principal component analysis. By learning the eigenstructure of each writer's handwriting, the writer's specific style can be determined. Through projection onto the eigenspace of each individual's handwriting, discriminative features are obtained and utilized in the recognition process. In this work, we use the sum of the strength of major eigenmodes as a similarity metric. From the twelve-people experiment conducted, the best writer identification rate obtained is 97.3% in average. The appealing results suggest that the proposed method is a promising approach