1,574 research outputs found
Deep learning investigation for chess player attention prediction using eye-tracking and game data
This article reports on an investigation of the use of convolutional neural
networks to predict the visual attention of chess players. The visual attention
model described in this article has been created to generate saliency maps that
capture hierarchical and spatial features of chessboard, in order to predict
the probability fixation for individual pixels Using a skip-layer architecture
of an autoencoder, with a unified decoder, we are able to use multiscale
features to predict saliency of part of the board at different scales, showing
multiple relations between pieces. We have used scan path and fixation data
from players engaged in solving chess problems, to compute 6600 saliency maps
associated to the corresponding chess piece configurations. This corpus is
completed with synthetically generated data from actual games gathered from an
online chess platform. Experiments realized using both scan-paths from chess
players and the CAT2000 saliency dataset of natural images, highlights several
results. Deep features, pretrained on natural images, were found to be helpful
in training visual attention prediction for chess. The proposed neural network
architecture is able to generate meaningful saliency maps on unseen chess
configurations with good scores on standard metrics. This work provides a
baseline for future work on visual attention prediction in similar contexts
Analysis of Gender Differences in Facial Expression Recognition Based on Deep Learning Using Explainable Artificial Intelligence
Potential uses of automated Facial Expression Recognition (FER) cover a wide range of applications such as customer behavior analysis, healthcare applications or providing personalized services. Data for machine learning play a fundamental role, therefore, understanding the relevancy of the data in the outcomes is of utmost importance. In this work we present a study on how gender influences the learning of a FER system. We analyze with Explainable Artificial intelligence (XAI) techniques how gender contributes to the learning and assess which facial expressions are more similar regarding face regions that impact on the classification.
Results show that there exist common regions in some expressions both for females and males with different intensities (e.g. happiness); however, there are other expressions like disgust, where important face regions differ. The insights of this work will help improving FER systems and understand the source of any inequality
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