4,914 research outputs found
Unifying Geometric Features and Facial Action Units for Improved Performance of Facial Expression Analysis
Previous approaches to model and analyze facial expression analysis use three
different techniques: facial action units, geometric features and graph based
modelling. However, previous approaches have treated these technique
separately. There is an interrelationship between these techniques. The facial
expression analysis is significantly improved by utilizing these mappings
between major geometric features involved in facial expressions and the subset
of facial action units whose presence or absence are unique to a facial
expression. This paper combines dimension reduction techniques and image
classification with search space pruning achieved by this unique subset of
facial action units to significantly prune the search space. The performance
results on the publicly facial expression database shows an improvement in
performance by 70% over time while maintaining the emotion recognition
correctness.Comment: 8 pages, ISBN: 978-1-61804-285-
Using Deep Autoencoders for Facial Expression Recognition
Feature descriptors involved in image processing are generally manually
chosen and high dimensional in nature. Selecting the most important features is
a very crucial task for systems like facial expression recognition. This paper
investigates the performance of deep autoencoders for feature selection and
dimension reduction for facial expression recognition on multiple levels of
hidden layers. The features extracted from the stacked autoencoder outperformed
when compared to other state-of-the-art feature selection and dimension
reduction techniques
Face Recognition: From Traditional to Deep Learning Methods
Starting in the seventies, face recognition has become one of the most
researched topics in computer vision and biometrics. Traditional methods based
on hand-crafted features and traditional machine learning techniques have
recently been superseded by deep neural networks trained with very large
datasets. In this paper we provide a comprehensive and up-to-date literature
review of popular face recognition methods including both traditional
(geometry-based, holistic, feature-based and hybrid methods) and deep learning
methods
Real-time Facial Expression Recognition "In The Wild'' by Disentangling 3D Expression from Identity
Human emotions analysis has been the focus of many studies, especially in the
field of Affective Computing, and is important for many applications, e.g.
human-computer intelligent interaction, stress analysis, interactive games,
animations, etc. Solutions for automatic emotion analysis have also benefited
from the development of deep learning approaches and the availability of vast
amount of visual facial data on the internet. This paper proposes a novel
method for human emotion recognition from a single RGB image. We construct a
large-scale dataset of facial videos (\textbf{FaceVid}), rich in facial
dynamics, identities, expressions, appearance and 3D pose variations. We use
this dataset to train a deep Convolutional Neural Network for estimating
expression parameters of a 3D Morphable Model and combine it with an effective
back-end emotion classifier. Our proposed framework runs at 50 frames per
second and is capable of robustly estimating parameters of 3D expression
variation and accurately recognizing facial expressions from in-the-wild
images. We present extensive experimental evaluation that shows that the
proposed method outperforms the compared techniques in estimating the 3D
expression parameters and achieves state-of-the-art performance in recognising
the basic emotions from facial images, as well as recognising stress from
facial videos. %compared to the current state of the art in emotion recognition
from facial images.Comment: to be published in 15th IEEE International Conference on Automatic
Face and Gesture Recognition (FG 2020
Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion Recognition
Automatic emotion recognition is a challenging task. In this paper, we
present our effort for the audio-video based sub-challenge of the Emotion
Recognition in the Wild (EmotiW) 2018 challenge, which requires participants to
assign a single emotion label to the video clip from the six universal emotions
(Anger, Disgust, Fear, Happiness, Sad and Surprise) and Neutral. The proposed
multimodal emotion recognition system takes audio, video and text information
into account. Except for handcraft features, we also extract bottleneck
features from deep neutral networks (DNNs) via transfer learning. Both temporal
classifiers and non-temporal classifiers are evaluated to obtain the best
unimodal emotion classification result. Then possibilities are extracted and
passed into the Beam Search Fusion (BS-Fusion). We test our method in the
EmotiW 2018 challenge and we gain promising results. Compared with the baseline
system, there is a significant improvement. We achieve 60.34% accuracy on the
testing dataset, which is only 1.5% lower than the winner. It shows that our
method is very competitive.Comment: 9 pages, 11 figures and 4 Tables. EmotiW2018 challeng
Automatic emotional state detection using facial expression dynamic in videos
In this paper, an automatic emotion detection system is built for a computer or machine to detect the emotional state from facial expressions in human computer communication. Firstly, dynamic motion features are extracted from facial expression videos and then advanced machine learning methods for classification and regression are used to predict the emotional states.
The system is evaluated on two publicly available datasets, i.e. GEMEP_FERA and AVEC2013, and satisfied performances are achieved in comparison with the baseline results provided. With this emotional state detection capability, a machine can read the facial expression of its user automatically. This technique can be integrated into applications such as smart robots, interactive games and smart surveillance systems
ACII 2009: Affective Computing and Intelligent Interaction. Proceedings of the Doctoral Consortium
This volume collects the contributions presented at the ACII 2009 Doctoral Consortium, the event aimed at gathering PhD students with the goal of sharing ideas about the theories behind affective computing; its development; and its application. Published papers have been selected out a large number of high quality submissions covering a wide spectrum of topics including the analysis of human-human, human-machine and human-robot interactions, the analysis of physiology and nonverbal behavior in affective phenomena, the effect of emotions on language and spoken interaction, and the embodiment of affective behaviors
Facial age estimation using BSIF and LBP
Human face aging is irreversible process causing changes in human face
characteristics such us hair whitening, muscles drop and wrinkles. Due to the
importance of human face aging in biometrics systems, age estimation became an
attractive area for researchers. This paper presents a novel method to estimate
the age from face images, using binarized statistical image features (BSIF) and
local binary patterns (LBP)histograms as features performed by support vector
regression (SVR) and kernel ridge regression (KRR). We applied our method on
FG-NET and PAL datasets. Our proposed method has shown superiority to that of
the state-of-the-art methods when using the whole PAL database.Comment: 5 pages, 8 figure
A comparative study on face recognition techniques and neural network
In modern times, face recognition has become one of the key aspects of
computer vision. There are at least two reasons for this trend; the first is
the commercial and law enforcement applications, and the second is the
availability of feasible technologies after years of research. Due to the very
nature of the problem, computer scientists, neuro-scientists and psychologists
all share a keen interest in this field. In plain words, it is a computer
application for automatically identifying a person from a still image or video
frame. One of the ways to accomplish this is by comparing selected features
from the image and a facial database. There are hundreds if not thousand
factors associated with this. In this paper some of the most common techniques
available including applications of neural network in facial recognition are
studied and compared with respect to their performance.Comment: 8 page
FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals
The recent proliferation of fake portrait videos poses direct threats on
society, law, and privacy. Believing the fake video of a politician,
distributing fake pornographic content of celebrities, fabricating impersonated
fake videos as evidence in courts are just a few real world consequences of
deep fakes. We present a novel approach to detect synthetic content in portrait
videos, as a preventive solution for the emerging threat of deep fakes. In
other words, we introduce a deep fake detector. We observe that detectors
blindly utilizing deep learning are not effective in catching fake content, as
generative models produce formidably realistic results. Our key assertion
follows that biological signals hidden in portrait videos can be used as an
implicit descriptor of authenticity, because they are neither spatially nor
temporally preserved in fake content. To prove and exploit this assertion, we
first engage several signal transformations for the pairwise separation
problem, achieving 99.39% accuracy. Second, we utilize those findings to
formulate a generalized classifier for fake content, by analyzing proposed
signal transformations and corresponding feature sets. Third, we generate novel
signal maps and employ a CNN to improve our traditional classifier for
detecting synthetic content. Lastly, we release an "in the wild" dataset of
fake portrait videos that we collected as a part of our evaluation process. We
evaluate FakeCatcher on several datasets, resulting with 96%, 94.65%, 91.50%,
and 91.07% accuracies, on Face Forensics, Face Forensics++, CelebDF, and on our
new Deep Fakes Dataset respectively. We also analyze signals from various
facial regions, under image distortions, with varying segment durations, from
different generators, against unseen datasets, and under several dimensionality
reduction techniques.Comment: To appear in IEEE Transactions on Pattern Analysis and Machine
Intelligence (PAMI), accepted July 2020. Dataset: http://bit.ly/FakeCatche
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