Facial Action Detection Using Block-Based Pyramid Appearance Descriptors

Facial expression is one of the most important non-verbal behavioural cues in social signals. Constructing an effective face representation from images is an essential step for successful facial behaviour analysis. Most existing face descriptors operate on the same scale, and do not leverage coarse v.s. fine methods such as image pyramids. In this work, we propose the sparse appearance descriptors Block-based Pyramid Local Binary Pattern (B-PLBP) and Block-based Pyramid Local Phase Quantisation (B-PLPQ). The effectiveness of our proposed descriptors is evaluated by a real-time facial action recognition system. The performance of B-PLBP and B-PLPQ is also compared with Block-based Local Binary Patterns (B-LBP) and Block-based Local Phase Quantisation (B-LPQ). The system proposed here enables detection a much larger range of facial behaviour by detecting 22 facial muscle actions (Action Units, AUs), which can be practically applied for social behaviour analysis and synthesis. Results show that our proposed descriptor B-PLPQ outperforms all other tested methods for the problem of FACS Action Unit analysis and that systems which utilise a pyramid representation outperform those that use basic appearance descriptors.