Comparison of global and local features for author's identification by using geometrical and zoning methods

Identification analysis for author's handwriting image in forensic investigation is still an important research area in this current big data era. Images feature extraction can lead to an issue of high dimensionality of data. The process of feature extraction is the most crucial process in author's identification. It is important to choose the best method to represent the image. This study compared two feature extraction methods, namely Higher-Order United Moment Invariant (HUMI) and the Edge-based Directional (ED) method that construct the Global and Local Features respectively. The additional process of discretization was implemented before the training and testing phase to represent the generalized features for the classifier models. This process induced a better performance accuracy for both methods where the discretized Local Features achieved 99.95% accuracy rate that slightly outperforms the discretized Global Features with only 99.91%

Geometrical Feature Based Ranking using Grey Relational Analysis (GRA) for Writer Identification

The author’s unique characteristic is determined by the variation of generated features from feature extraction process. Different sets of features produced are based on different feature extraction methods (local or global). Thus, the process has led to the production of high dimensional datasets that contributing to many irrelevant or redundant features. The main problem however is to find a way to identify the most significant features. The features ranking method using Grey Relational Analysis (GRA) is proposed to find the significance of each feature and give ranking to the features. This study presents the Higher-Order United Moment Invariant (HUMI) as the global feature extraction methods. The combinations of features with the higher ranking are discretized and used as the subsets of features to identify the writer. The result demonstrates that the average classification accuracy of five classifiers by using just the combination of two most significant features have yielded a better performance than using all features