Multiclass Functional Discriminant Analysis and Its Application to Gesture Recognition

We consider applying a functional logistic discriminant procedure to the analysis of handwritten character data. Time-course trajectories corresponding to the X and Y coordinate values of handwritten characters written in the air with one finger are converted into a functional data set via regularized basis expansion. We then apply functional logistic modeling to classify the functions into several classes. In order to select the values of adjusted parameters involved in the functional logistic model, we derive a model selection criterion for evaluating models estimated by the method of regularization. Results indicate the effectiveness of our modeling strategy in terms of prediction accuracy.