19,604 research outputs found
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis
Cross-domain synthesizing realistic faces to learn deep models has attracted
increasing attention for facial expression analysis as it helps to improve the
performance of expression recognition accuracy despite having small number of
real training images. However, learning from synthetic face images can be
problematic due to the distribution discrepancy between low-quality synthetic
images and real face images and may not achieve the desired performance when
the learned model applies to real world scenarios. To this end, we propose a
new attribute guided face image synthesis to perform a translation between
multiple image domains using a single model. In addition, we adopt the proposed
model to learn from synthetic faces by matching the feature distributions
between different domains while preserving each domain's characteristics. We
evaluate the effectiveness of the proposed approach on several face datasets on
generating realistic face images. We demonstrate that the expression
recognition performance can be enhanced by benefiting from our face synthesis
model. Moreover, we also conduct experiments on a near-infrared dataset
containing facial expression videos of drivers to assess the performance using
in-the-wild data for driver emotion recognition.Comment: 8 pages, 8 figures, 5 tables, accepted by FG 2019. arXiv admin note:
substantial text overlap with arXiv:1905.0028
Learning Grimaces by Watching TV
Differently from computer vision systems which require explicit supervision,
humans can learn facial expressions by observing people in their environment.
In this paper, we look at how similar capabilities could be developed in
machine vision. As a starting point, we consider the problem of relating facial
expressions to objectively measurable events occurring in videos. In
particular, we consider a gameshow in which contestants play to win significant
sums of money. We extract events affecting the game and corresponding facial
expressions objectively and automatically from the videos, obtaining large
quantities of labelled data for our study. We also develop, using benchmarks
such as FER and SFEW 2.0, state-of-the-art deep neural networks for facial
expression recognition, showing that pre-training on face verification data can
be highly beneficial for this task. Then, we extend these models to use facial
expressions to predict events in videos and learn nameable expressions from
them. The dataset and emotion recognition models are available at
http://www.robots.ox.ac.uk/~vgg/data/facevalueComment: British Machine Vision Conference (BMVC) 201
EmoNets: Multimodal deep learning approaches for emotion recognition in video
The task of the emotion recognition in the wild (EmotiW) Challenge is to
assign one of seven emotions to short video clips extracted from Hollywood
style movies. The videos depict acted-out emotions under realistic conditions
with a large degree of variation in attributes such as pose and illumination,
making it worthwhile to explore approaches which consider combinations of
features from multiple modalities for label assignment. In this paper we
present our approach to learning several specialist models using deep learning
techniques, each focusing on one modality. Among these are a convolutional
neural network, focusing on capturing visual information in detected faces, a
deep belief net focusing on the representation of the audio stream, a K-Means
based "bag-of-mouths" model, which extracts visual features around the mouth
region and a relational autoencoder, which addresses spatio-temporal aspects of
videos. We explore multiple methods for the combination of cues from these
modalities into one common classifier. This achieves a considerably greater
accuracy than predictions from our strongest single-modality classifier. Our
method was the winning submission in the 2013 EmotiW challenge and achieved a
test set accuracy of 47.67% on the 2014 dataset
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