Fuzzy Integral for Combining SVM-Based Handwritten Soft-Biometrics Prediction

This work addresses soft-biometrics prediction from handwriting analysis, which aims to predict the writer's gender, age range and handedness. Three SVM predictors associated each to a specific data feature are developed and subsequently combined to aggregate a robust prediction. For the combination step, Sugeno's Fuzzy Integral is proposed. Experiments are conducted on public Arabic and English handwriting datasets. The performance assessment is carried out comparatively to individual systems as well as to max and average rules, using independent and blended corpuses. The results obtained demon-strated the usefulness of the Fuzzy Integral, which provides a gain of more than 4% over individual systems as well as other combination rules. Moreover, with respect to the state of the art methods, the proposed approach seems to be much more relevant.