In this paper we describe a technique for joint estimation of head pose and multiple soft biometrics from faces (Age, Gender and Ethnicity). Our proposed Multi-Objective Random Forests (MORF) framework is a unified model for the joint estimation of multiple characteristics that automatically adapts the measure of information gain used for evaluating the quality of weak learners. Since facial characteristics are related in the feature space, estimating all of them jointly can be beneficial as trees can learn to condition the estimation of some characteristics on others. We reformulate the splitting criterion of random trees in our multi-objective formulation and evaluate it on publicly available face characteristic estimation imagery. These preliminary experiments show promising results.