2,067 research outputs found
Facial emotion recognition using min-max similarity classifier
Recognition of human emotions from the imaging templates is useful in a wide
variety of human-computer interaction and intelligent systems applications.
However, the automatic recognition of facial expressions using image template
matching techniques suffer from the natural variability with facial features
and recording conditions. In spite of the progress achieved in facial emotion
recognition in recent years, the effective and computationally simple feature
selection and classification technique for emotion recognition is still an open
problem. In this paper, we propose an efficient and straightforward facial
emotion recognition algorithm to reduce the problem of inter-class pixel
mismatch during classification. The proposed method includes the application of
pixel normalization to remove intensity offsets followed-up with a Min-Max
metric in a nearest neighbor classifier that is capable of suppressing feature
outliers. The results indicate an improvement of recognition performance from
92.85% to 98.57% for the proposed Min-Max classification method when tested on
JAFFE database. The proposed emotion recognition technique outperforms the
existing template matching methods
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 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
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