JeFaPaTo -- A joint toolbox for blinking analysis and facial features extraction
This work addresses the challenge of making facial analysis tools accessible to medical experts who lack programming expertise, though it is incremental as it applies existing methods in a new domain.
The paper tackled the problem of analyzing facial features and blinking in medical contexts, particularly for facial palsy, by developing JeFaPaTo, a user-friendly toolbox that simplifies access to advanced computer vision algorithms for medical professionals without programming skills.
Analyzing facial features and expressions is a complex task in computer vision. The human face is intricate, with significant shape, texture, and appearance variations. In medical contexts, facial structures and movements that differ from the norm are particularly important to study and require precise analysis to understand the underlying conditions. Given that solely the facial muscles, innervated by the facial nerve, are responsible for facial expressions, facial palsy can lead to severe impairments in facial movements. One affected area of interest is the subtle movements involved in blinking. It is an intricate spontaneous process that is not yet fully understood and needs high-resolution, time-specific analysis for detailed understanding. However, a significant challenge is that many computer vision techniques demand programming skills for automated extraction and analysis, making them less accessible to medical professionals who may not have these skills. The Jena Facial Palsy Toolbox (JeFaPaTo) has been developed to bridge this gap. It utilizes cutting-edge computer vision algorithms and offers a user-friendly interface for those without programming expertise. This toolbox makes advanced facial analysis more accessible to medical experts, simplifying integration into their workflow.