The human face is a powerful channel for emotional expression, making it central to how we communicate and connect. As robotics continues to evolve, a growing array of tools and applications are being developed to recognize and/or mimic facial expressions—bringing machines closer to truly intuitive, human-like interaction. However, the human face changes significantly over time due to aging. The impact of synthetic facial aging on emotion recognition has been underexplored. This study investigates how synthetic facial wrinkles—used to simulate aging on the synthetic faces—affect emotion perception in both human observers and automated facial expression recognition algorithms. We generated synthetic faces representing diverse ethnicities and genders, applying varying degrees of synthetic facial wrinkles. In Study 1, we validated the perceived age of these faces across ethnic and gender groups. In Study 2, we examined the recognition of six basic emotions (happy, surprise, fear, disgust, anger, and sad) by both human participants and a facial expression recognition algorithm. Our results show that while human participants remained relatively unaffected by aging in recognizing most emotions, the algorithm demonstrated significant performance variability, with increased wrinkle intensity improving the detection of certain emotions (e.g., disgust, anger) while degrading recognition of others (e.g., sad, surprise). These findings underscore the need for more diverse and age-inclusive training data in facial expression recognition systems and highlight the importance of considering facial aging effects in both emotion AI design and social robot development. We discuss the implications for creating more inclusive, emotionally intelligent robots that reflect the complexity of human aging.