Objectives: I introduced a novel protocol based on an AI-assisted analytic system for facial expressions, Customized Precision Facial Assessment (CPFA), to evaluate and quantify the micro-expressions of aesthetic concern. With the help of CPFA, physicians may be able to conduct static and dynamic assessments for the micro-expressions of the patients and perform quantitative measurements before and after the treatments.
Introduction: Becoming more attractive is one of the most important reasons to receive cosmetic treatments 1. Attractiveness is strongly associated with facial expressions 2 which also contribute to the first impression 3. And a happy facial expression is usually connected to positive mood and looks more attractive. In contrast, sad and angry facial expressions are considered negative and being less attractive. Therefore, enhancing the positive facial features and reducing the negative ones is a nice strategy of beautification.
Materials / method: Customized Precision Facial Assessment (CPFA) comprises of static and dynamic analyses: At first, patients were instructed to make no facial expression for 30 seconds for static analysis while their facial action units were continuously monitored by CPFA. And the patients were then asked to make six basic facial expressions for subsequent dynamic analysis, including disgust, sadness, happiness, fear, anger and surprise. With CPFA, the muscle actions leading to these expressions were analyzed and marked by facial action coding system.
Results: Neuromodulators and injectable fillers have been used to sooth wrinkles, facial creases, restore volume loss and address excessive muscle movement. However, precise evaluation before treatment is crucial to natural and successful result.
Customized Precision Facial Assessment (CPFA), the novel protocol based on FaceReader, is the first aesthetic application of the well-established system in psychiatry. Through the detection of micro-expressions and its active action units of facial muscles, physicians are more likely to optimize the treatment with minimal intervention by precise localization o
Conclusion: I proposed Customized Precision Facial Assessment (CPFA)—a novel protocol based on an AI-assisted analytic system to unveil and quantify the static and dynamic facial micro-expressions for advanced aesthetic treatment. This pilot study demonstrates that CPFA can objectively recognize and quantify the facial action units associated with negative emotions and the physician may be able to customize the treatment for individuals accordingly with promising results. Further studies are needed to validate and explore the potential use of this system.
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