A Trainable Feature Extractor Module for Deep Neural Networks and Scanpath Classification
This work addresses scanpath classification for applications in medicine, manufacturing, and training systems, but it appears incremental as it builds on classical histogram-based approaches.
The authors tackled the problem of scanpath classification by proposing a trainable feature extraction module for deep neural networks, which adapts its parameters based on backpropagated error to improve performance, and they evaluated it on three public datasets with comparisons to state-of-the-art approaches.
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.