Facial expression recognition technology has been utilized both for entertainment purposes and as a valuable aid in rehabilitation and facial exercise assistance. This technology leverages artificial intelligence models to predict facial landmark points and provide visual feedback, thereby facilitating users’ facial movements. However, feedback designs that disregard user preferences may cause discomfort and diminish the benefits of exercise. This study aimed to develop a feedback design guide for facial rehabilitation exercises by investigating user responses to various feedback design methods. We created a facial recognition mobile application and designed six feedback variations based on shape and transparency. To evaluate user experience, we conducted a usability test involving 48 participants (24 subjects in their 20s and 24 over 60 years of age), assessing factors such as feedback, assistance, disturbance, aesthetics, cognitive ease, and appropriateness. The experimental results revealed significant differences in transparency, age, and the interaction between transparency and age. Consequently, it is essential to consider both transparency and user age when designing facial recognition feedback. The findings of this study could potentially inform the design of more effective and personalized visual feedback for facial motion, ultimately benefiting users in rehabilitation and exercise contexts.