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

Libro de actas. XXXV Congreso Anual de la Sociedad Española de Ingeniería Biomédica

596 p.CASEIB2017 vuelve a ser el foro de referencia a nivel nacional para el intercambio científico de conocimiento, experiencias y promoción de la I D i en Ingeniería Biomédica. Un punto de encuentro de científicos, profesionales de la industria, ingenieros biomédicos y profesionales clínicos interesados en las últimas novedades en investigación, educación y aplicación industrial y clínica de la ingeniería biomédica. En la presente edición, más de 160 trabajos de alto nivel científico serán presentados en áreas relevantes de la ingeniería biomédica, tales como: procesado de señal e imagen, instrumentación biomédica, telemedicina, modelado de sistemas biomédicos, sistemas inteligentes y sensores, robótica, planificación y simulación quirúrgica, biofotónica y biomateriales. Cabe destacar las sesiones dedicadas a la competición por el Premio José María Ferrero Corral, y la sesión de competición de alumnos de Grado en Ingeniería biomédica, que persiguen fomentar la participación de jóvenes estudiantes e investigadores