Correlates between social attention and personality traits have been widely acknowledged in social psychology studies. Head pose has commonly been employed as a proxy for determining the social attention direction in small group interactions. However, the impact of head pose estimation errors on personality estimates has not been studied to our knowledge. In this work, we consider the unstructured and dynamic cocktail party scenario where the scene is captured by multiple, large field-of-view cameras. Head pose estimation is a challenging task under these conditions owing to the uninhibited motion of persons (due to which facial appearance varies owing to perspective and scale changes), and the low resolution of captured faces. Based on proxemic and social attention features computed from position and head pose annotations, we first demonstrate that social attention features are excellent predictors of the Extraversion and Neuroticism personality traits. We then repeat classification experiments with behavioral features computed from automated estimates - obtained experimental results show that while prediction performance for both traits is affected by head pose estimation errors, the impact is more adverse for Extraversion.