  This paper presents a novel approach for synthesizing facial affect; either
in terms of the six basic expressions (i.e., anger, disgust, fear, joy, sadness
and surprise), or in terms of valence (i.e., how positive or negative is an
emotion) and arousal (i.e., power of the emotion activation). The proposed
approach accepts the following inputs: i) a neutral 2D image of a person; ii) a
basic facial expression or a pair of valence-arousal (VA) emotional state
descriptors to be generated, or a path of affect in the 2D VA Space to be
generated as an image sequence. In order to synthesize affect in terms of VA,
for this person, $600,000$ frames from the 4DFAB database were annotated. The
affect synthesis is implemented by fitting a 3D Morphable Model on the neutral
image, then deforming the reconstructed face and adding the inputted affect,
and blending the new face with the given affect into the original image.
Qualitative experiments illustrate the generation of realistic images, when the
neutral image is sampled from thirteen well known lab-controlled or in-the-wild
databases, including Aff-Wild, AffectNet, RAF-DB; comparisons with Generative
Adversarial Networks (GANs) show the higher quality achieved by the proposed
approach. Then, quantitative experiments are conducted, in which the
synthesized images are used for data augmentation in training Deep Neural
Networks to perform affect recognition over all databases; greatly improved
performances are achieved when compared with state-of-the-art methods, as well
as with GAN-based data augmentation, in all cases.
