Three dimensional binary edge feature representation for pain expression analysis

Automatic pain expression recognition is a challenging task for pain assessment and diagnosis. Conventional 2D-based approaches to automatic pain detection lack robustness to the moderate to large head pose variation and changes in illumination that are common in real-world settings and with few exceptions omit potentially informative temporal information. In this paper, we propose an innovative 3D binary edge feature (3D-BE) to represent high-resolution 3D dynamic facial expression. To exploit temporal information, we apply a latent-dynamic conditional random field approach with the 3D-BE. The resulting pain expression detection system proves that 3D-BE represents the pain facial features well, and illustrates the potential of noncontact pain detection from 3D facial expression data.