A novel approach on infant facial pain classification using multi stage classifier and geometrical-textural features combination

Infants are unable to communicate pain, they cry to express their pain. In this paper we describe the most effective feature for infant facial pain classification. The image dataset was classified by medical doctors and nurses based on cortisol hormone difference and FLACC (Face, Legs, Activity, Cry, Consolability) measurement. In this paper we try a number of features based on Action Unit (AU) for infant facial pain classification and discover that the best features are combination between geometrical and textural features. We trained our own Active Shape Model (ASM) and extracted the geometrical features based on landmark points found by our ASM. The textural features are extracted using Local Binary Patterns (LBP) from multiple facial patches. We also experiment with two stage pain classification preceded by a cry detection system, and concluded that this scenario combined with geometrical and textural feature produce a very high F1 score for infant facial pain classification.