Abstract. Automatic pain recognition is an evolving research area with promising applications in health care. In this paper, we propose the first fully automatic approach to continuous pain intensity estimation from facial images. We first learn a set of independent regression functions for continuous pain intensity estimation using different shape (facial landmarks) and appearance (DCT and LBP) features, and then perform their late fusion. We show on the recently published UNBC-MacMaster Shoulder Pain Expression Archive Database that late fusion of the afore-mentioned features leads to better pain intensity estimation compared to feature-specific pain intensity estimation.
In REF Kaltwang et al. propose what they claim to be the first fully automatic continuous pain intensity method.
Continuous pain intensity estimation from facial expressions
In Proceedings of the International Symposium on Visual Computing
Computer Science