A Trainable Feature Extractor Module for Deep Neural Networks and
  Scanpath Classification
Wolfgang Fuhl
Scanpath classification is an area in eye tracking research with possible
applications in medicine, manufacturing as well as training systems for
students in various domains. In this paper we propose a trainable feature
extraction module for deep neural networks. The purpose of this module is to
transform a scanpath into a feature vector which is directly useable for the
deep neural network architecture. Based on the backpropagated error of the deep
neural network, the feature extraction module adapts its parameters to improve
the classification performance. Therefore, our feature extraction module is
jointly trainable with the deep neural network. The motivation to this feature
extraction module is based on classical histogram-based approaches which
usually compute distributions over a scanpath. We evaluated our module on three
public datasets and compared it to the state of the art approaches.