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Assessing the Efficacy of Deep Learning Approaches for Facial Expression Recognition in Individuals with Intellectual Disabilities
Our examination of the outcomes, both the performance and the important image regions for the models, reveals significant distinctions in facial expressions between individuals with and without intellectual disabilities, as well as among individuals with intellectual disabilities.
Explainable artificial intelligence
Facial Expression Recognition
Unveiling the Human-like Similarities of Automatic Facial Expression Recognition: An Empirical Exploration through Explainable AI
We employ an innovative global explainable AI method to generate heatmaps, revealing crucial facial regions for the twelve networks trained on six facial expressions.
Local Agnostic Video Explanations: a Study on the Applicability of Removal-Based Explanations to Video
Our contributions include: (1) Extending a fine-grained explanation framework tailored for computer vision data, (2) Adapting six existing explanation techniques to work on video data by incorporating temporal information and enabling local explanations, and (3) Conducting an evaluation and comparison of the adapted explanation methods using different models and datasets.
Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review
In this study, a systematic review of the literature is presented on fall detection and Human Activity Recognition (HAR) for the elderly, as the two main tasks to solve to guarantee the safety of elderly people living alone.
Assessing Gender Bias in Predictive Algorithms using eXplainable AI
Predictive algorithms have a powerful potential to offer benefits in areas as varied as medicine or education.
Facial Expression Recognition
Facial Expression Recognition (FER)
