37,663 research outputs found
Ensemble of Hankel Matrices for Face Emotion Recognition
In this paper, a face emotion is considered as the result of the composition
of multiple concurrent signals, each corresponding to the movements of a
specific facial muscle. These concurrent signals are represented by means of a
set of multi-scale appearance features that might be correlated with one or
more concurrent signals. The extraction of these appearance features from a
sequence of face images yields to a set of time series. This paper proposes to
use the dynamics regulating each appearance feature time series to recognize
among different face emotions. To this purpose, an ensemble of Hankel matrices
corresponding to the extracted time series is used for emotion classification
within a framework that combines nearest neighbor and a majority vote schema.
Experimental results on a public available dataset shows that the adopted
representation is promising and yields state-of-the-art accuracy in emotion
classification.Comment: Paper to appear in Proc. of ICIAP 2015. arXiv admin note: text
overlap with arXiv:1506.0500
Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression
We present techniques for improving performance driven facial animation,
emotion recognition, and facial key-point or landmark prediction using learned
identity invariant representations. Established approaches to these problems
can work well if sufficient examples and labels for a particular identity are
available and factors of variation are highly controlled. However, labeled
examples of facial expressions, emotions and key-points for new individuals are
difficult and costly to obtain. In this paper we improve the ability of
techniques to generalize to new and unseen individuals by explicitly modeling
previously seen variations related to identity and expression. We use a
weakly-supervised approach in which identity labels are used to learn the
different factors of variation linked to identity separately from factors
related to expression. We show how probabilistic modeling of these sources of
variation allows one to learn identity-invariant representations for
expressions which can then be used to identity-normalize various procedures for
facial expression analysis and animation control. We also show how to extend
the widely used techniques of active appearance models and constrained local
models through replacing the underlying point distribution models which are
typically constructed using principal component analysis with
identity-expression factorized representations. We present a wide variety of
experiments in which we consistently improve performance on emotion
recognition, markerless performance-driven facial animation and facial
key-point tracking.Comment: to appear in Image and Vision Computing Journal (IMAVIS
Using facial expression recognition for crowd monitoring.
Master of Science in Engineering. University of KwaZulu-Natal, Durban 2017.In recent years, Crowd Monitoring techniques have attracted emerging interest in the
eld of computer vision due to their ability to monitor groups of people in crowded
areas, where conventional image processing methods would not suffice. Existing
Crowd Monitoring techniques focus heavily on analyzing a crowd as a single entity,
usually in terms of their density and movement pattern. While these techniques are
well suited for the task of identifying dangerous and emergency situations, such as a
large group of people exiting a building at once, they are very limited when it comes
to identifying emotion within a crowd. By isolating different types of emotion within
a crowd, we aim to predict the mood of a crowd even in scenes of non-panic.
In this work, we propose a novel Crowd Monitoring system based on estimating
crowd emotion using Facial Expression Recognition (FER). In the past decade, both
FER and activity recognition have been proposed for human emotion detection.
However, facial expression is arguably more descriptive when identifying emotion
and is less likely to be obscured in crowded environments compared to body pos-
ture. Given a crowd image, the popular Viola and Jones face detection algorithm
is used to detect and extract unobscured faces from individuals in the crowd. A ro-
bust and efficient appearance based method of FER, such as Gradient Local Ternary
Pattern (GLTP), is used together with a machine learning algorithm, Support Vec-
tor Machine (SVM), to extract and classify each facial expression as one of seven
universally accepted emotions (joy, surprise, anger, fear, disgust, sadness or neutral
emotion). Crowd emotion is estimated by isolating groups of similar emotion based
on their relative size and weighting.
To validate the effectiveness of the proposed system, a series of cross-validation
tests are performed using a novel Crowd Emotion dataset with known ground-truth
emotions. The results show that the system presented is able to accurately and
efficiently predict multiple classes of crowd emotion even in non-panic situations
where movement and density information may be incomplete. In the future, this
type of system can be used for many security applications; such as helping to alert
authorities to potentially aggressive crowds of people in real-time
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