PCA-based gender classification system using hybridization of features and classification techniques

Gender classification system is an automated, challenging, and efficacious system due to the analogous vision of males and females with handwriting. Gender classification is a binary problem that is based on physiological and behavioral biometric traits. It is widely used for forensic-based investigations, autopsy determination, suspected areas, and questioned documents. The proposed study has been considered for the behavioral biometric trait, i.e., handwriting in the Gurumukhi script. The novelty of this study can be seen from the three perspectives, i.e., hybridization of feature extraction techniques, principal component analysis (PCA) for dimensionality reduction, and hybridization of classification techniques. Zoning, Diagonal, Transition, and Peak Extent-based feature extractions were implemented followed by their hybridization. The classification techniques such as Decision Trees, Random Forest, and Extreme Gradient Boosting classifiers were experienced with the hybridized approach using a majority voting scheme. Also, the comparison and critical analysis of gender classification accuracy with CPU elapsed time has also been presented before and after implementing PCA. There are many novel ideas for the researchers such as gender classification through online handwriting, prediction of age, personality, state, nationality, and stress prediction through handwriting, and further gender classification problem can be enhanced to third or trans-gender too using handwriting as a biometric modality. The system can be implemented with many other Indic scripts and presenting novel track in the handwriting-based researches.