17,756 research outputs found
Affective Facial Expression Processing via Simulation: A Probabilistic Model
Understanding the mental state of other people is an important skill for
intelligent agents and robots to operate within social environments. However,
the mental processes involved in `mind-reading' are complex. One explanation of
such processes is Simulation Theory - it is supported by a large body of
neuropsychological research. Yet, determining the best computational model or
theory to use in simulation-style emotion detection, is far from being
understood.
In this work, we use Simulation Theory and neuroscience findings on
Mirror-Neuron Systems as the basis for a novel computational model, as a way to
handle affective facial expressions. The model is based on a probabilistic
mapping of observations from multiple identities onto a single fixed identity
(`internal transcoding of external stimuli'), and then onto a latent space
(`phenomenological response'). Together with the proposed architecture we
present some promising preliminary resultsComment: Annual International Conference on Biologically Inspired Cognitive
Architectures - BICA 201
The color of smiling: computational synaesthesia of facial expressions
This note gives a preliminary account of the transcoding or rechanneling
problem between different stimuli as it is of interest for the natural
interaction or affective computing fields. By the consideration of a simple
example, namely the color response of an affective lamp to a sensed facial
expression, we frame the problem within an information- theoretic perspective.
A full justification in terms of the Information Bottleneck principle promotes
a latent affective space, hitherto surmised as an appealing and intuitive
solution, as a suitable mediator between the different stimuli.Comment: Submitted to: 18th International Conference on Image Analysis and
Processing (ICIAP 2015), 7-11 September 2015, Genova, Ital
Tensor-on-tensor regression
We propose a framework for the linear prediction of a multi-way array (i.e.,
a tensor) from another multi-way array of arbitrary dimension, using the
contracted tensor product. This framework generalizes several existing
approaches, including methods to predict a scalar outcome from a tensor, a
matrix from a matrix, or a tensor from a scalar. We describe an approach that
exploits the multiway structure of both the predictors and the outcomes by
restricting the coefficients to have reduced CP-rank. We propose a general and
efficient algorithm for penalized least-squares estimation, which allows for a
ridge (L_2) penalty on the coefficients. The objective is shown to give the
mode of a Bayesian posterior, which motivates a Gibbs sampling algorithm for
inference. We illustrate the approach with an application to facial image data.
An R package is available at https://github.com/lockEF/MultiwayRegression .Comment: 33 pages, 3 figure
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