2 research outputs found
A Personalized Affective Memory Neural Model for Improving Emotion Recognition
Recent models of emotion recognition strongly rely on supervised deep
learning solutions for the distinction of general emotion expressions. However,
they are not reliable when recognizing online and personalized facial
expressions, e.g., for person-specific affective understanding. In this paper,
we present a neural model based on a conditional adversarial autoencoder to
learn how to represent and edit general emotion expressions. We then propose
Grow-When-Required networks as personalized affective memories to learn
individualized aspects of emotion expressions. Our model achieves
state-of-the-art performance on emotion recognition when evaluated on
\textit{in-the-wild} datasets. Furthermore, our experiments include ablation
studies and neural visualizations in order to explain the behavior of our
model.Comment: Accepted by the International Conference on Machine Learning 2019
(ICML2019
Temporal aggregation of audio-visual modalities for emotion recognition
Emotion recognition has a pivotal role in affective computing and in
human-computer interaction. The current technological developments lead to
increased possibilities of collecting data about the emotional state of a
person. In general, human perception regarding the emotion transmitted by a
subject is based on vocal and visual information collected in the first seconds
of interaction with the subject. As a consequence, the integration of verbal
(i.e., speech) and non-verbal (i.e., image) information seems to be the
preferred choice in most of the current approaches towards emotion recognition.
In this paper, we propose a multimodal fusion technique for emotion recognition
based on combining audio-visual modalities from a temporal window with
different temporal offsets for each modality. We show that our proposed method
outperforms other methods from the literature and human accuracy rating. The
experiments are conducted over the open-access multimodal dataset CREMA-D