Automatic Human Health Monitoring Based on Computer Vision has gained rapid scientific attention in the decade, fuelled by a large number of research articles and commercial systems based on set of features, extracted from face and gesture. Consequently, researchers from computer vision, as well as from medical science community have granted significant attention, with goals ranging from patient analysis and monitoring to diagnostics. (e.g., for dementia [1, 2], depression [3], healthcare [4], physiological measurement [5, 6], apathy [10], heart rate [9], rare neurologic diseases [8]). Moreover, healthcare represents an area of broad economic (e.g.,, social, and scientific impact.

Hence, to elevate the state of the art and to attract the attention of researchers, we organized a related special session within the 14th IEEE International Conference on Automatic Face and Recognition (IEEE AFGR 2019). However, a number of challenges remain open, such as the limited robustness of current techniques in real world scenario, limited size datasets, as well as the heterogeneity of the acquisition environment. Due to the overwhelming success of the previous version of the special session we plan to host a successive session in conjunction with IEEE AFGR 2020. We aim to document recent advancements in automated healthcare, as well as enable and discuss progress. Therefore, the goal of this special session is to bring together researchers and practitioners working in this area of computer vision and medical science, and to address a wide range of theoretical and practical issues related to real-life healthcare systems.

Topics of interest include, but are not limited to:

  • Health monitoring based on face analysis,
  • Health monitoring based on gesture analysis,
  • Health monitoring based corporeal-based visual features,
  • Depression analysis based on visual features,
  • Face analytics for human behaviour understanding,
  • Anxiety diagnosis based on face and gesture,
  • Physiological measurement employing face analytics,
  • Databases on health monitoring, e.g., depression analysis,
  • Augmentative and alternative communication,
  • Human-robot interaction,
  • Home healthcare,
  • Technology for cognition,
  • Automatic emotional hearing and understanding,
  • Visual attention and visual saliency,
  • Assistive living,
  • Privacy preserving systems,
  • Quality of life technologies,
  • Mobile and wearable systems,
  • Applications for the visually impaired,
  • Sign language recognition and applications for hearing impaired,
  • Applications for the ageing society,
  • Personalized monitoring,
  • Egocentric and first-person vision,
  • Applications to improve health and wellbeing of children and elderly.


  • Dr. Antitza Dantcheva (Inria Sophia Antipolis, France)
  • Dr. Abhijit Das (ISI, University of Southern California)
  • Dr. François Brémond (Inria Sophia Antipolis, France)
  • A/Prof. Hu Han (ICT, Chinese Academy of Sciences)
  • Prof. Xilin Chen (ICT, Chinese Academy of Sciences)