Synthesis of Realistic Facial Expressions Using Expression Map

Abstract

For synthesis of realistic facial expressions displaying emotions, we need an efficient representation of pure (e.g., surprise ) as well as mixed (e.g., happily surprised ) emotional expressions. In this paper, we train an expression map (XM) that efficiently represents the emotional expressions. We propose an algorithm that utilizes the XM to synthesize emotional expressions, tailor-made for the facial structure of the target person. The proposed method can also control the proportions of different basic emotional expressions of those, when mixed together, to generate realistic emotional facial expressions. Unlike many existing methods, our expression synthesis model requires only one expression-neutral face image of the target person. Both qualitative and quantitative tests on four data sets show promising results. On average, we have achieved 92.4% correct validation of the expressions synthesized by our method. We also show that for both basic and mixed emotional expressions, our method generates finer expression details compared to existing state-of-the-art works.