Face reading technology: improving preference prediction from self-reports using micro-expressions

Traditional market research primarily relies on self-reports (SR) to assess consumer preferences, yet these methods are prone to biases and limited in capturing subconscious emotional responses. Psychophysiological and neurophysiological methods offer objective alternatives, but their high cost and intrusiveness limit practical use. This study examines automated Facial Expression Analysis (FEA), focusing on micro-expression (ME) emotion data, as a scalable, non-intrusive approach to improve the accuracy of predicting consumer choices. In a controlled experiment exposing participants to both video and poster ads, we compare the predictive power of ME and SR emotion data using machine learning and artificial neural network models. Results demonstrate that ME data significantly enhance both multinomial and binomial choice prediction accuracy compared to SR data, particularly for dynamic video ads where ME patterns capture real-time emotional fluctuations more effectively. Beyond its methodological contributions, our study underscores practical implications and discusses the ethical considerations related to consumer privacy.