UIBVFEDPlus-Light: Virtual facial expression dataset with lighting.

It is well-known that lighting conditions have an important influence on the automatic recognition of human expressions. Although the impact of lighting on the perception of emotions has been studied in different works, databases of facial expressions do not consider intentional lighting. In this work, a new database of facial expressions performed by virtual characters with four different lighting configurations is presented. This database, named UIBVFEDPlus-Light, is an extension of the previously published UIBVFED virtual facial expression dataset. It includes 100 characters, four lighting configurations and a software application that allows one to interactively visualize the expressions, and manage their intensity and lighting condition. Also, an experience of use is described to show how this work can raise new challenges to facial expression and emotion recognition techniques under usual lighting environments. Thus, opening new study perspectives in this area.

Understanding How CNNs Recognize Facial Expressions: A Case Study with LIME and CEM.

Recognizing facial expressions has been a persistent goal in the scientific community. Since the rise of artificial intelligence, convolutional neural networks (CNN) have become popular to recognize facial expressions, as images can be directly used as input. Current CNN models can achieve high recognition rates, but they give no clue about their reasoning process. Explainable artificial intelligence (XAI) has been developed as a means to help to interpret the results obtained by machine learning models. When dealing with images, one of the most-used XAI techniques is LIME. LIME highlights the areas of the image that contribute to a classification. As an alternative to LIME, the CEM method appeared, providing explanations in a way that is natural for human classification: besides highlighting what is sufficient to justify a classification, it also identifies what should be absent to maintain it and to distinguish it from another classification. This study presents the results of comparing LIME and CEM applied over complex images such as facial expression images. While CEM could be used to explain the results on images described with a reduced number of features, LIME would be the method of choice when dealing with images described with a huge number of features.

UIBVFED: Virtual facial expression dataset.

Facial expression classification requires large amounts of data to reflect the diversity of conditions in the real world. Public databases support research tasks providing researchers an appropriate work framework. However, often these databases do not focus on artistic creation. We developed an innovative facial expression dataset that can help both artists and researchers in the field of affective computing. This dataset can be managed interactively by an intuitive and easy to use software application. The dataset is composed of 640 facial images from 20 virtual characters each creating 32 facial expressions. The avatars represent 10 men and 10 women, aged between 20 and 80, from different ethnicities. Expressions are classified by the six universal expressions according to Gary Faigin classification.

PROGame: A process framework for serious game development for motor rehabilitation therapy.

Serious game development for rehabilitation therapy is becoming increasingly popular because of the motivational advantages that these types of applications provide. Consequently, the need for a common process framework for this category of software development has become increasingly evident. The goal is to guarantee that products are developed and validated by following a coherent and systematic method that leads to high-quality serious games. This paper introduces a new process framework for the development of serious games for motor rehabilitation therapy. We introduce the new model and demonstrate its application for the development of a serious game for the improvement of the balance and postural control of adults with cerebral palsy. The development of this application has been facilitated by two technological transfer contracts and is being exploited by two different organizations. According to clinical measurements, patients using the application improved from high fall risk to moderate fall risk. We believe that our development strategy can be useful not only for motor rehabilitation therapy, but also for the development of serious games in many other rehabilitation areas.