Affect recognition & generation in-the-wild

Affect recognition based on a subject’s facial expressions has been a topic of major research in the attempt to generate machines that can understand the way subjects feel, act and react. In the past, due to the unavailability of large amounts of data captured in real-life situations, research has mainly focused on controlled environments. However, recently, social media and platforms have been widely used. Moreover, deep learning has emerged as a means to solve visual analysis and recognition problems. This Ph.D. Thesis exploits these advances and makes significant contributions for affect analysis and recognition in-the-wild. We tackle affect analysis and recognition as a dual knowledge generation problem: i) we create new, large and rich in-the-wild databases and ii) we design and train novel deep neural architectures that are able to analyse affect over these databases and to successfully generalise their performance on other datasets. At first, we present the creation of Aff-Wild database annotated according to valence-arousal and an end-to-end CNN-RNN architecture, AffWildNet. Then we use AffWildNet as a robust prior for dimensional and categorical affect recognition and extend it by extracting low-/mid-/high-level latent information and analysing this via multiple RNNs. Additionally, we propose a novel loss function for DNN-based categorical affect recognition. Next, we generate Aff-Wild2, the first database containing annotations for all main behavior tasks: estimate Valence-Arousal; classify into Basic Expressions; detect Action Units. We develop multi-task and multi-modal extensions of AffWildNet by fusing these tasks and propose a novel holistic approach that utilises all existing databases with non-overlapping annotations and couples them through co-annotation and distribution matching. Finally, we present an approach for valence-arousal, or basic expressions’ facial affect synthesis. We generate an image with a given affect, or a sequence of images with evolving affect, by annotating a 4-D database and utilising a 3-D morphable model.Open Acces

Deep neural network augmentation: generating faces for affect analysis

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 fifteen 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