10,602 research outputs found
User independent Emotion Recognition with Residual Signal-Image Network
User independent emotion recognition with large scale physiological signals
is a tough problem. There exist many advanced methods but they are conducted
under relatively small datasets with dozens of subjects. Here, we propose
Res-SIN, a novel end-to-end framework using Electrodermal Activity(EDA) signal
images to classify human emotion. We first apply convex optimization-based EDA
(cvxEDA) to decompose signals and mine the static and dynamic emotion changes.
Then, we transform decomposed signals to images so that they can be effectively
processed by CNN frameworks. The Res-SIN combines individual emotion features
and external emotion benchmarks to accelerate convergence. We evaluate our
approach on the PMEmo dataset, the currently largest emotional dataset
containing music and EDA signals. To the best of author's knowledge, our method
is the first attempt to classify large scale subject-independent emotion with
7962 pieces of EDA signals from 457 subjects. Experimental results demonstrate
the reliability of our model and the binary classification accuracy of 73.65%
and 73.43% on arousal and valence dimension can be used as a baseline
ICface: Interpretable and Controllable Face Reenactment Using GANs
This paper presents a generic face animator that is able to control the pose
and expressions of a given face image. The animation is driven by human
interpretable control signals consisting of head pose angles and the Action
Unit (AU) values. The control information can be obtained from multiple sources
including external driving videos and manual controls. Due to the interpretable
nature of the driving signal, one can easily mix the information between
multiple sources (e.g. pose from one image and expression from another) and
apply selective post-production editing. The proposed face animator is
implemented as a two-stage neural network model that is learned in a
self-supervised manner using a large video collection. The proposed
Interpretable and Controllable face reenactment network (ICface) is compared to
the state-of-the-art neural network-based face animation techniques in multiple
tasks. The results indicate that ICface produces better visual quality while
being more versatile than most of the comparison methods. The introduced model
could provide a lightweight and easy to use tool for a multitude of advanced
image and video editing tasks.Comment: Accepted in WACV-202
Frustration recognition from speech during game interaction using wide residual networks
ABSTRACT
Background Although frustration is a common emotional reaction during playing games, an excessive level of frustration can harm users’ experiences, discouraging them from undertaking further game interactions. The automatic detection of players’ frustration enables the development of adaptive systems, which through a real-time difficulty adjustment, would adapt the game to the user’s specific needs; thus, maximising players experience and guaranteeing the game success. To this end, we present our speech-based approach for the automatic detection of frustration during game interactions, a specific task still under-explored in research. Method The experiments were performed on the Multimodal Game Frustration Database (MGFD), an audiovisual dataset—collected within the Wizard-of-Oz framework—specially tailored to investigate verbal and facial expressions of frustration during game interactions. We explored the performance of a variety of acoustic feature sets, including Mel-Spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs), as well as the low dimensional knowledge-based acoustic feature set eGeMAPS. Due to the always increasing improvements achieved by the use of Convolutional Neural Networks (CNNs) in speech recognition tasks, unlike the MGFD baseline—based on Long Short-Term Memory (LSTM) architecture and Support Vector Machine (SVM) classifier—in the present work we take into consideration typically used CNNs, including ResNets, VGG, and AlexNet. Furthermore, given the still open debate on the shallow vs deep networks suitability, we also examine the performance of two of the latest deep CNNs, i. e., WideResNets and EfficientNet. Results Our best result, achieved with WideResNets and Mel-Spectrogram features, increases the system performance from 58.8 % Unweighted Average Recall (UAR) to 93.1 % UAR for speech-based automatic frustration recognition
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