3 research outputs found
Automatic analysis of facilitated taste-liking
This paper focuses on: (i) Automatic recognition of taste-liking
from facial videos by comparatively training and evaluating models
with engineered features and state-of-the-art deep learning
architectures, and (ii) analysing the classification results along the
aspects of facilitator type, and the gender, ethnicity, and personality
of the participants. To this aim, a new beverage tasting dataset
acquired under different conditions (human vs. robot facilitator
and priming vs. non-priming facilitation) is utilised. The experimental
results show that: (i) The deep spatiotemporal architectures
provide better classification results than the engineered feature
models; (ii) the classification results for all three classes of liking,
neutral and disliking reach F1 scores in the range of 71%-91%; (iii)
the personality-aware network that fuses participants’ personality
information with that of facial reaction features provides improved
classification performance; and (iv) classification results vary across
participant gender, but not across facilitator type and participant
ethnicity.EPSR