Prototype-Based Modeling for <newline/>Facial Expression Analysis

Abstract

Automatic facial expression analysis systems are aiming towards the application of computer vision techniques in human computer interaction, emotion analysis, and even medical care via a space mapping between the continuous emotion and a set of discrete expression categories. The main difficulty with these systems is the inherent problem of facial alignment due to person-specific appearance. Beside the facial representation problem, the same displayed facial expression may vary differently across humans; this can be true even for the same person in different contexts. To cope with these variable factors, we introduce the concept of prototype-based model as anchor modeling through a SIFT-flow registration. A set of prototype facial expression models is generated as a reference space of emotions on which face images are projected to generate a set of registered faces. To characterize the facial expression appearance, oriented gradients are processed on each registered image. We obtained the best results 87% with the person–independent evaluation strategy on JAFFE dataset (7-class expression recognition problem), and 83% on the complex setting of the GEMEP-FERA database (5-class problem).