Online Writer Identification Using The Point Distribution Model

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