Dissimilarity Gaussian Mixture Models for Efficient Offline Handwritten Text-Independent Identification Using SIFT and RootSIFT Descriptors

Handwriting biometrics is the science of identifying the behavioral aspect of an individual's writing style and exploiting it to develop automated writer identification and verification systems. This paper presents an efficient handwriting identification system which combines scale-invariant feature transform (SIFT) and RootSIFT descriptors in a set of Gaussian mixture models (GMMs). In particular, a new concept of similarity and dissimilarity Gaussian mixture models (SGMM and DGMM) is introduced. While an SGMM is constructed for every writer to describe the intra-class similarity that is exhibited between the handwritten texts of the same writer, a DGMM represents the contrast or dissimilarity that exists between the writer's style on one hand and other different handwriting styles on the other hand. Furthermore, because the handwritten text is described by a number of key point descriptors where each descriptor generates an SGMM/DGMM score, a new weighted histogram method is proposed to derive the intermediate prediction score for each writer's GMM. The idea of weighted histogram exploits the fact that handwritings from the same writer should exhibit more similar textual patterns than dissimilar ones, hence, by penalizing the bad scores with a cost function, the identification rate can be significantly enhanced. Our proposed system has been extensively assessed using six different public datasets (including three English, two Arabic, and one hybrid language), and the results have shown the superiority of the proposed system over the state-of-the-art techniques.