Personality detection is an important task in psychology, as different personality traits are linked to different behaviors and real-life outcomes. Traditionally it involves filling out lengthy questionnaires which is time consuming, and may also be unreliable if respondents do not fully understand the questions or are not willing to honestly answer them. In this talk, we will present our framework for objective personality detection that leverages humans’ physiological responses to external stimuli. We exemplify and evaluate the framework in a case study, where we expose subjects to affective image and video stimuli, and capture their physiological responses using non-invasive commercial-grade eye-tracking and skin conductivity sensors. These responses are then processed and used to build a machine learning classifier capable of accurately predicting a wide range of personality traits. We investigate and discuss the performance of various machine learning methods, the most and least accurately predicted traits, and also assess the importance of the different stimuli, features, and physiological signals. Our work demonstrates that personality traits can be accurately detected, suggesting the applicability of the proposed framework for robust personality detection and use by psychology practitioners and researchers, as well as designers of personalized interactive systems.