Deep Learning-Based Facial Expression Recognition for the Elderly: A Systematic Review
Journal:
arXiv
Published Date:
Feb 4, 2025
Abstract
The rapid aging of the global population has highlighted the need for
technologies to support elderly, particularly in healthcare and emotional
well-being. Facial expression recognition (FER) systems offer a non-invasive
means of monitoring emotional states, with applications in assisted living,
mental health support, and personalized care. This study presents a systematic
review of deep learning-based FER systems, focusing on their applications for
the elderly population. Following a rigorous methodology, we analyzed 31
studies published over the last decade, addressing challenges such as the
scarcity of elderly-specific datasets, class imbalances, and the impact of
age-related facial expression differences. Our findings show that convolutional
neural networks remain dominant in FER, and especially lightweight versions for
resource-constrained environments. However, existing datasets often lack
diversity in age representation, and real-world deployment remains limited.
Additionally, privacy concerns and the need for explainable artificial
intelligence emerged as key barriers to adoption. This review underscores the
importance of developing age-inclusive datasets, integrating multimodal
solutions, and adopting XAI techniques to enhance system usability,
reliability, and trustworthiness. We conclude by offering recommendations for
future research to bridge the gap between academic progress and real-world
implementation in elderly care.
