In e-learning environments, most adaptive systems do not consider the learner's emotional state when recommending activities for learning difficulties, blockages, or demotivation. In this paper, we propose a new approach of emotion-based adaptation in e-learning environments. The system will allow recommendation resources/activities to motivate and support the learner in learning. Our first contribution is modeling the learner's emotion by exploiting the facial expressions generated during the pedagogical activities. For this purpose, a probability-based emotion quantification algorithm has been proposed. To recommend support resources, we presented our adaptation approach that leverages a set of adopted adaptation criteria, where the weighting of these criteria differs from one support resource to another. Five experiments aimed at validating the approach were conducted on two groups of students (test and control groups). The results show our approach's impact on improving the learner's cognitive level, engagement time, and motivation.