Most of the facial expression recognition methods consider that both training and testing data are equally distributed. As facial image sequences may contain information for heterogeneous sources, facial data may be asymmetrically distributed between training and testing, as it may be difficult to maintain the same quality and quantity of information. In this work, we present a novel classification method based on the learning using privileged information (LUPI) paradigm to address the problem of facial expression recognition. We introduce a probabilistic classification approach based on conditional random fields (CRFs) to indirectly propagate knowledge from privileged to regular feature space. Each feature space owns specific parameter settings, which are combined together through a Gaussian prior, to train the proposed t-CRF+ model and allow the different tasks to share parameters and improve classification performance. The proposed method is validated on two challenging and publicly available benchmarks on facial expression recognition and improved the state-of-the-art methods in the LUPI framework.